System and method for mode switching

ABSTRACT

Systems and methods described provide dynamic and intelligent ways to change the required level of user interaction during use of a monitoring device. The systems and methods generally relate to real time switching between a first or initial mode of user interaction and a second or new mode of user interaction. In some cases, the switching will be automatic and transparent to the user, and in other cases user notification may occur. The mode switching generally affects the user&#39;s interaction with the device, and not just internal processing. The mode switching may relate to calibration modes, data transmission modes, control modes, or the like.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 14/862,079 filed Sep. 22, 2015, which claims the benefit of U.S.Provisional Application No. 62/053,733, filed Sep. 22, 2014. Theaforementioned application is incorporated by reference herein in itsentirety, and is hereby expressly made a part of this specification.

TECHNICAL FIELD

The present embodiments relate to continuous analyte monitoring, and, inparticular, to control of operation of an analyte monitor upon changesin available data in a continuous analyte monitoring system.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin-dependent) and/or in which insulinis not effective (Type II or non-insulin-dependent). In the diabeticstate, the patient or user suffers from high blood sugar, which cancause an array of physiological derangements associated with thedeterioration of small blood vessels, for example, kidney failure, skinulcers, or bleeding into the vitreous of the eye. A hypoglycemicreaction (low blood sugar) can be induced by an inadvertent overdose ofinsulin, or after a normal dose of insulin or glucose-lowering agentaccompanied by extraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a personwith diabetes normally only measures his or her glucose levels two tofour times per day. Unfortunately, such time intervals are so far spreadapart that the person with diabetes likely finds out too late of ahyperglycemic or hypoglycemic condition, sometimes incurring dangerousside effects. It is not only unlikely that a person with diabetes willbecome aware of a dangerous condition in time to counteract it, but itis also likely that he or she will not know whether his or her bloodglucose concentration value is going up (higher) or down (lower) basedon conventional methods. Diabetics thus may be inhibited from makingeducated insulin therapy decisions.

Another device that some diabetics used to monitor their blood glucoseis a continuous analyte sensor, e.g., a continuous glucose monitor(CGM). A CGM typically includes a sensor that is placed invasively,minimally invasively or non-invasively. The sensor measures theconcentration of a given analyte within the body, e.g., glucose, andgenerates a raw signal using electronics associated with the sensor. Theraw signal is converted into an output value that is rendered on adisplay. The output value that results from the conversion of the rawsignal is typically expressed in a form that provides the user withmeaningful information, and in which form users have become familiarwith analyzing, such as blood glucose expressed in mg/dL.

The above discussion assumes the output value is reliable and true, andthe same generally requires a significant degree of user interaction toensure proper calibration. For example, current CGMs rely heavily onuser interaction, for example, using blood glucose meter readings toconfirm glucose concentration values before dosing insulin. However,additional user action adds a significant source of error in themonitoring and reduces convenience by requiring more action of the userthan desired.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY

Systems and methods according to present principles meet the needs ofthe above in several ways. In particular, the systems and methodsprovide dynamic and intelligent ways to change the required level ofuser interaction during use of the monitoring device, e.g., over thecourse of the sensor session, as dictated by the usability of the deviceas well as in some cases user choice. The usability of the device isoften influenced by the usability of the data received from the sensor.Such changes may increase or decrease the level and type of userinteraction, depending on usability of sensor data and also oftenaccording to other available data, but generally are intended todynamically reduce the level of user interaction based on the desired orneeded usability of sensor data.

The systems and methods described here generally relate to real timeswitching between a first or initial mode of user interaction and asecond or new mode of user interaction. In some cases, the switchingwill be automatic and transparent to the user, and in other cases usernotification (or request for confirmation) may occur. The mode switchingmay cause a switch from a first mode to a second mode, followed by aswitch to a third mode or back to the first or second modes. In anycase, the mode switching will generally affect the user's interactionwith the device, and not just cause internal processing changes withinthe device, although such processing changes will generally accompanythe mode switching.

In many cases the decision or trigger to switch between modes relates tothe usability of a sensor signal, as compared to a transition criteria,but may also be based on other data, combined (or not) with usabilitydata, such as the value of the sensor signal, external data, and thelike.

In one implementation, an analyte monitoring device may transition orswitch types of calibration modes, e.g., from a user-dependentcalibration to a device self-calibration mode, i.e., a calibrationroutine using blood glucose concentration values from an external meterto a calibration routine performed by the device itself (i.e., withoutreal time external reference values). In another implementation, ananalyte monitoring device may transition or switch types of datatransmission modes, e.g., from providing information or data on-demand(e.g., upon user demand) to a mode in which information or datatransmission is initiated by the device, e.g., as a regular or irregularperiodic communication and/or in response to a trigger such as a largeexcursion. In some cases, as described below, such is termed a modeswitch from scheduled transmissions to unscheduled transmissions. In yetanother implementation, an analyte monitoring device may transition orswitch from one type of decision-support mode to another, e.g., from atherapeutic use to a non-therapeutic use (e.g., adjunctive use) or moregranularly from one mode or phase of control to another, includingproviding educational information versus providing therapeuticinformation. More generally, an analyte monitoring device may beconfigured to switch between modes or phases of control, such asdescribed in greater detail below with respect to FIG. 15A. In all ofthese implementations, the mode transition or switches may generally beperformed in both directions, and in the cases of multiple phases ofcontrol, between various phases of control, both sequential andnonsequential.

A number of triggers will be described, and generally the triggers arebased on one or more criteria, e.g., where a determined parameter orvariable (“determined data”) meets, exceeds, matches, or otherwise bearsa predetermined relationship with a threshold, i.e., a predeterminedtransition threshold criterion or criteria, or is determined orpredicted to do so in the future. The determined parameter or variablemay be data associated with the sensor signal, i.e., the sensor signalvalue or a scaled representation thereof, data about the sensor signal,e.g., data from signal analysis indicating its noise level or the like,data from an external source, e.g., data from a blood glucose meter,temperature sensor, clock, location sensor, or the like, as well asother data as will be described below.

Systems and methods according to present principles may use one or moreof many different triggers, i.e., data and accompanying transitioncriteria, on which to base mode switching. In some cases, for specifictypes of mode switching, particular determined parameters or variableswill be especially useful. For example, data usability may be especiallypertinent when deciding and switching calibration modes ordecision-support modes. The signal value itself may be especiallypertinent when deciding data or information transmission modes. However,these are purely exemplary and it will be understood that in givenimplementations other criteria may prove useful.

In one aspect, the embodiments are directed towards a method ofoperating a continuous glucose monitoring device, the continuous glucosemonitoring device coupled to a glucose sensor and operating in aninitial mode of operation, including: measuring a signal indicative ofglucose concentration data; displaying the glucose concentration data ona user interface of the continuous glucose monitoring device, the userinterface in the initial mode of operation having an initial mode ofuser interaction; determining data indicative of a usability of thecontinuous glucose monitoring device; comparing the determined data toone or more transition criteria; if the comparing indicates thedetermined data has met or will meet the transition criteria, causingthe continuous glucose monitoring device to transition to a new mode ofoperation; and displaying the glucose concentration data on the userinterface of the continuous glucose monitoring device. The userinterface in the new mode of operation has a different mode of userinteraction than the initial mode, such that the continuous glucosemonitoring device operates in a mode of user interaction according tothe device usability.

Implementations of the embodiments may include one or more of thefollowing. The displaying may be based at least in part on the mode ofoperation. The determining data may include receiving data from thesensor. The receiving data from the sensor may include receiving datafrom a sensor electronics module coupled to the sensor. The sensor maybe configured for in vivo insertion into the patient. A first output ofthe monitoring device in the initial mode of operation may represent theinitial mode of user interaction and a second output of the monitoringdevice in the new mode of operation may represent the new mode of userinteraction, and the first and second outputs may be different. Theinitial and new modes of user interaction may be configured such thatthe new mode of user interaction requires less user interaction than theinitial mode of user interaction. The initial and new modes of userinteraction may be selected from the group consisting of: user-dependentcalibration and device self-calibration. The analyte may be glucose andthe user-dependent calibration may correspond to entry of a calibrationvalue from an external blood glucose meter. The initial and new modes ofuser interaction may include levels of confirmation interactions. Theanalyte may be glucose and the initial and new modes of user interactionmay include different levels of decision support selected from the groupconsisting of: non-therapeutic (adjunctive), therapeutic(non-adjunctive), and phases of control in an artificial pancreassystem. The analyte may be glucose and the initial and new modes of userinteraction may be data transmission modes selected from the groupconsisting of on-demand data transmission and device-initiated datatransmission. The initial and new modes of user interaction may beselected from the group consisting of: pushed data or pulled data. Thedetermined data may include an analyte concentration value and/or a timerate of change thereof.

The determined data indicative of the usability of the device and thetransition criteria may include one or more parameters indicative of theusability of a signal from the sensor, such as one or more parametersselected from the group consisting of accuracy, reliability, stability,confidence, and/or glycemic urgency index. The one or more parametersrelated to the usability of the signal may correspond to a level ofnoise or to one or more faults detected in the signal, and thetransition criteria may be a threshold level of noise or a predeterminedtype or level of fault, which may be determined based on a long-termtrend of the signal, a short-term trend of the signal, or on a historyof a user's previous sensor session. The one or more parameters relatedto the usability of the signal may correspond to one or more of thegroup consisting of: signal value, a range of signal values, or a timerate of change thereof; analyte concentration value or range of values;calibration data; a measured error at calibration; data fromself-diagnostics or calibration diagnostics; metadata about sensoridentity; environmental data corresponding to a sensor; historicalpattern data; external data; data about frequency of calibration;biological data about sensor placement; a time duration since sensorimplantation; an impedance associated with the signal; a received userresponse to a prompt displayed on a user interface; a decision supportmode; a data transmission mode; data about a selected use of themonitoring device; data about clinical or user goals; or combinations ofthe above.

For example, the environmental data may correspond to altitude ortemperature data about a sensor environment. The calibration data may beselected from the group consisting of: calibration values, confidence incalibration values, uncertainty in calibration values, range ofcalibration values, rate of change of calibration values, currentcalibration values compared to historical calibration values, stabilityin calibration values, whether calibration values match expected orpredicted values, confidence in a user's ability to accurately entercalibration values from a meter, whether entered calibration datacorresponds to a default or pre-entered value, or combinations of theabove. The historical pattern data may include data about reboundvariability.

The external data may be from an activity monitor, a sleep monitor, amedicament pump, GPS device, a redundant analyte sensor, a smart pen, orcombinations of the above. The biological data about sensor placementmay correspond to data about: tissue type, wound response, diffusiondistance, or combinations of the above. The diffusion distance may beproportional to one or more selected from the group consisting of:impedance, thickness of membrane over electrode array, oxygen depletionrate, diffusion of specific species between electrodes, or combinationsof the above. The decision support mode may be selected from the groupconsisting of: non-therapeutic (adjunctive), therapeutic(non-adjunctive), and different levels of control of an artificialpancreas system. The data about a selected use of the monitoring devicemay include data about uses selected from the group consisting of:weight loss monitoring, monitoring exercise or activity impact onglucose, post-meal glucose summary, food selection, effect of themonitored analyte on illness or menstrual cycle or pregnancy, userpreference or convenience, or combinations of the above. The data aboutclinical or user goals may include: data about user knowledge of device,desired accuracy of device, desired convenience of device, use of devicefor hypoglycemic avoidance, use of device for nighttime control, use ofdevice for postprandial control, qualitative or quantitative desiredduration of sensor session, or combinations of the above. The desiredconvenience of the device may correspond to a number of requiredexternal meter calibration values.

The initial mode may be user-dependent calibration, and before thecausing step, the method may further include causing the device toperiodically and temporarily enter a self-calibration mode, tointerrogate the sensor and to examine a transient response, followed bya re-entering of the user-dependent calibration initial mode. The methodmay further include displaying output data based on the new mode. Themethod may further include displaying an indication of an expectedduration of the new mode. The method may further include displaying anindication of sensor performance.

Certain implementations may particularly apply to calibration mode. Forexample, the initial mode may be user-dependent calibration and the newmode may be device self-calibration; or the initial mode may be deviceself-calibration and the new mode may be user-dependent calibration. Thedetermined data may be sensor signal or data usability and thetransition criteria may be a threshold level of sensor signal or datausability. The transition criteria may be further dependent on adecision support mode, the decision support mode may be selected fromthe group consisting of adjunctive (non-therapeutic), therapeutic(non-adjunctive), or a phase or mode of control in an artificialpancreas system. The transition criteria may be further dependent ondata entered or received about a user or clinician use of informationdisplayed by the monitoring device.

A decision support mode associated with the initial mode may betherapeutic and a decision support mode associated with the new mode maybe adjunctive, and the determined data may be such that the sensorsignal usability decreased below the threshold level of sensor signalusability associated with the transition criterion. The method mayfurther include: prompting a user on a periodic basis to enter acalibration value from an external meter for blood glucose; andreceiving the calibration value for blood glucose. The periodicity maybe less in the new mode than in the initial mode.

A decision support mode associated with the initial mode may beadjunctive and a decision support mode associated with the new mode maybe therapeutic, and the determined data may be such that the sensorsignal usability increased above the threshold level of sensor signalusability associated with the transition criterion. The method mayfurther include: prompting a user on a periodic basis to enter acalibration value for blood glucose; and receiving the calibration valuefor blood glucose. The periodicity may be greater in the new mode thanin the initial mode.

The method may further include determining an intended mode of themonitoring device. The determining may include detecting whether amedicament delivery device is coupled to the monitoring device, and ifso, configuring the monitoring device to a mode that is therapeutic. Thedetermining may include: prompting a user to indicate an intended use ofthe monitoring device; receiving the indication; and configuring themonitoring device to a mode associated with the received indication. Anumber of blood glucose calibration readings required of the user may bebased on the configured mode. Where the intended use is therapeutic, themethod may further include configuring the monitoring device to auser-dependent calibration mode. Where the intended use is adjunctive,the method may further include configuring the monitoring device to adevice self-calibration mode.

Where the initial mode is device self-calibration and the new mode isuser-dependent calibration, the method may further include: prompting auser to enter a calibration value for blood glucose; receiving thecalibration value for blood glucose; and using the received calibrationvalue to inform the device self-calibration. The received calibrationvalue may inform the device self-calibration by modifying the deviceself-calibration. The initial mode may be device self-calibration andthe new mode may be user-dependent calibration, and the determined dataand the transition criteria may include one or more parameters relatedto the usability of a signal from the sensor, where the one or moreparameters are selected from the group consisting of: data fromdiagnostic routines indicating a shift in sensitivity; data entered by auser about a perceived error; data from a connected device; data fromhistoric analyte values; time of day; a day of the week; whether aglucose value is high or low as compared to respective thresholds; aglucose urgency index; data about glucose concentration valuevariability; data about a level of user responsiveness; sensor signalvalue trajectory pre-and post-insertion of a new sensor; redundant oroverlapping sensor data; user feedback on alerts and alarms; meal orexercise data as compared to predicted signal responses to meal orexercise data; data about a decision support mode configured for themonitoring device; or combinations of the above.

In the above, the data from diagnostic routines may include impedancedata detecting shifts in sensitivity. The diagnostic routines may beperformed on a periodic basis or upon detection of an error. The dataentered by a user about a perceived error may include a blood glucosecalibration value entered by a user in the absence of a prompt from themonitoring device, or a detection of a greater-than-average number ofblood glucose calibration values entered by a user. The data from aconnected device may include data from an external blood glucose meter.The initial mode may be device self-calibration and the new mode may beuser-dependent calibration, and the method may further include: if thecomparing indicates the determined data has met or will meet thetransition criteria, then before the causing step, prompting a user toenter a reason for the determined data; receiving the reason for thedetermined data; and based on the received reason, causing themonitoring device to maintain the initial mode of operation. The reasonmay be a user-perceived outlier, a user-perceived false alarm, or mealor exercise data.

The method may further include comparing the entered meal or exercisedata to prior user-entered meal or exercise data, comparing a currentsignal to a signal associated with the prior user-entered meal orexercise data, and determining if the current signal and entered meal orexercise data are consistent with the prior signal and prior meal orexercise data. The initial mode may be device self-calibration and thenew mode may be user-dependent calibration, and the method may furtherinclude: determining if a number of blood glucose measurements taken andentered into the monitoring device as calibration values exceed apredetermined threshold over a predetermined period of time, and if so,causing the monitoring device to transition to a user-dependentcalibration mode.

The initial mode may be user-dependent and the new mode may be deviceself-calibration, the transition criteria may correspond to a level ofconfidence in the device self-calibration, and the method may furtherinclude: prompting a user to enter a calibration value for bloodglucose, and using the entered value as the determined data; and if thecomparing indicates the determined data meets the transition criteria,then performing the causing (a mode transition) step. The initial modemay be user-dependent calibration and the new mode may be deviceself-calibration, and the determined data and the transition criteriamay correspond to the usability of entered blood glucose data, where theusability of entered blood glucose data corresponds to an accuracy,reliability, stability, or confidence in the blood glucose data. Themethod may further include confirming that entered blood glucose data iswithin a particular confidence interval or stability criterion, and ifit is not, then performing the causing step. The method may furtherinclude confirming that entered blood glucose data is within an expectedrange based on an a priori or internal calibration, and if it is not,then performing the causing step.

The transition criteria may be based at least in part on a decisionsupport mode in which the device is configured. The determined data andthe transition criteria may indicate that the device continues torequire external reference data for calibration, and the method mayfurther include maintaining the initial mode. The determined data andthe transition criteria may indicate that the device no longer requiresexternal reference data for calibration, and the method may furtherinclude performing the causing step. The method may further include apackage of sensors manufactured from the same lot, and the sensor may bea first of a plurality of sensors in the pack. In this case, thedetermined data and the transition criteria may indicate that the deviceno longer requires external reference data for calibration, and themethod may further include: performing the causing step of causing themonitoring device to transition to a new mode of operation; and forsubsequent sensors in the pack, initializing the device in deviceself-calibration mode, using one or more calibration settings associatedwith the first sensor.

The method may further include: initializing the monitoring device intwo modes simultaneously, a first mode being user-dependent calibrationand a second mode being device self-calibration; receiving and comparingtwo glucose concentration values, one glucose concentration value fromthe first mode and another glucose concentration value from the secondmode; determining and displaying a glucose concentration value based onthe two glucose concentration values; determining a level of confidencein the glucose concentration value from the second mode, using at leastthe two glucose concentration values; and once the determined level ofconfidence in the glucose concentration value from the second modereaches a predetermined threshold, then only displaying the glucoseconcentration value from the second mode. The determining a level ofconfidence in the glucose concentration value from the second mode mayinclude comparing at least the glucose concentration value from thesecond mode to a calibration value from an external meter.

The method may further include detecting a fault, and upon detection ofthe fault, displaying the glucose concentration value according to thefirst mode. The comparing may include comparing results of diagnostictests or internal calibration information. The internal calibrationinformation may be based on an impedance measurement. The predeterminedthreshold may be based at least in part on a decision support mode inwhich the device is configured. The comparing may include comparingslope and baseline information for the two modes. The comparing mayfurther include: comparing errors in slope and baseline data for each ofthe two modes; and once the error in the slope or baseline for thesecond mode is equivalent to that in the first mode, then onlydisplaying the glucose concentration value from the second mode. Thecomparing may further include determining slope and baseline informationfor each of the two modes with respective slope and baseline informationfor each of the two modes from a prior session. The method may furtherinclude displaying an indication of when a calibration value from anexternal meter is required.

The method may further include: initializing the monitoring device intwo parallel modes, a first mode being user-dependent calibration and asecond mode being device self-calibration; receiving and comparing twoglucose concentration values, one glucose concentration value from thefirst mode and another glucose concentration value from the second mode;providing a weighting of the two glucose concentration values; anddisplaying a glucose concentration value according to the weightedglucose concentration values. The weighting may be proportional to theusability of the data determined by each of the modes. Once theweighting for a given mode reaches a predetermined threshold, theglucose concentration value displayed may be determined based on onlythe given mode.

In some implementations, the modes correspond to a mode of decisionsupport. For example, the determined data may correspond to a sensorsignal, and the transition criteria may correspond at least to ausability of the sensor signal. The transition criteria may be at leastin part based on the initial mode of operation. The initial mode may bea therapeutic mode, and the new mode may be an adjunctive mode. Thedisplaying in the new mode of operation may further include, while inthe adjunctive mode, displaying data to a user in such a way as toindicate its usability adjunctively. The displaying in the new mode ofoperation may further include indicating the usability of the data bydisplaying a zone or range of glycemic data instead of a single value.The displaying in the new mode of operation may further includerequiring the user to clear a prompt before displaying a subsequentglucose concentration value or a range of glucose concentration values.The usability may be indicated by colors and/or flashing numerals and/ora dot size on a trend graph. The displaying in the new mode of operationmay further include restricting displayed data to only a rate of changearrow and not a glucose concentration value. The usability may beindicated by a displayed change in a prediction horizon. The displayingin the new mode of operation may further include, while in thetherapeutic mode, displaying data to a user in such a way as to indicateits usability therapeutically. The displaying in the new mode ofoperation may further include indicating the usability of the data bydisplaying a determined single value of glucose concentration. Theusability may be indicated by a displayed change in a predictionhorizon. The usability may be indicated by colors and/or flashingnumerals and/or a dot size on a trend graph. The transition criteria maybe further at least partially based on time of day or day of week. Theusability of the sensor signal may be based on one or more parametersselected from the group consisting of: a user response to a query abouta perceived accuracy or perceived user glucose range; data aboutlikelihood of a potential fault or failure mode; data about glucosecontext; a user response to a query about a glycemic event; a userresponse to a query about a potential false alarm; a confirmatory meterreading requested of a user via a displayed prompt; a calibration mode;a data transmission mode; a user indication of desired monitoring deviceresponsiveness; or combinations of the above.

The method may further include changing a calibration mode along withthe change from the initial to the new mode of operation. The method mayfurther include transmitting a signal to a medicament delivery pump. Thenew mode may be therapeutic, and the signal may instruct the pump toreceive and follow signals from the monitoring device. The new mode maybe adjunctive, and the signal may instruct the pump to disregardreceived signals from the monitoring device. The new mode may betherapeutic, and the signal may instruct the pump to receive and followsignals from the monitoring device to control the user glucoseconcentration value to a target value. The new mode may be therapeutic,and the signal may instruct the pump to receive and follow signals fromthe monitoring device to control the user glucose concentration value toa target range of values. The new mode may be therapeutic, and thesignal may instruct the pump to receive and follow signals from themonitoring device to control the user glucose concentration value onlywhen the glucose concentration value is below a predetermined value,above a predetermined value, or within a predetermined range of values.

The initial mode may be adjunctive and the new mode may be therapeutic,and in the new mode the monitoring device may be configured to calculatea recommended insulin bolus and the displaying on the user interface mayfurther include displaying the calculated recommended insulin boluswithout a calibration meter reading, and in the initial mode themonitoring device may be configured to not calculate and display arecommended insulin bolus without a calibration meter reading. Upon astep of sensor start up, the initial mode may be adjunctive, and thedisplaying indicating the new mode of operation may further includedisplaying low-resolution data.

The method may further include determining a level of confidence in thesensor over a period of time, and once the measured level of confidencehas reached a predetermined threshold, the method may further includethe step of displaying high-resolution data and causing a transition toa therapeutic mode. The determining a level of confidence may includereceiving an external blood glucose meter reading. The external bloodglucose meter reading may correlate to what the monitoring deviceestimates the glucose concentration value to be or may be used tocalibrate the monitoring device. The method may further includeconfiguring the monitoring device to enter a user-dependent calibrationmode of operation. The method may further include receiving an externalblood glucose meter reading, developing a level of confidence in thesensor over a period of time, and once the level of confidence hasreached a predetermined threshold, causing the monitoring device toenter a user-dependent calibration mode of operation.

The monitoring device may operate in two modes of operationconcurrently, one adjunctive and one therapeutic, and the displaying mayfurther include displaying an initial splash screen with data displayedin the adjunctive mode of operation. Upon receiving a selection from auser interface for data requiring a new mode of operation, the methodmay further include causing a transition to the new mode of operation,receiving one or more data values required by the new mode of operation,and displaying the data using the new mode of operation. The selecteddata may include a hypoglycemic safety alarm, and the new mode ofoperation may be user-dependent calibration.

Certain implementations of the embodiments may pertain to transmissionmodes. For example, the determined data may include data based on aglucose concentration value, and the transition criteria may be selectedfrom the group consisting of: a glycemic state threshold, a GUIthreshold, a glucose threshold, a glucose rate of change threshold, aglucose acceleration threshold, a predicted value of glucose or any ofits rates of change, an excursion beyond a predetermined threshold, analert criteria, a criteria for a glycemic danger zone, or a combinationof the above.

The transition criteria may be selected from the group consisting of: aduration of time since a user last requested a glucose concentrationvalue, a decision support mode, a user response to a query, acalibration mode of the monitoring device, or a combination of theabove. The determining data may include transmitting a signal to cause asensor to send a glucose concentration value. The determining data mayinclude receiving a signal from a sensor corresponding to a glucoseconcentration value. The initial mode may be on-demand transmission, thenew mode may be device-initiated transmission, the determined data maybe a glucose concentration value, and the transition criteria may be theglucose concentration value being in a dangerous range for a periodexceeding a first predetermined duration of time.

The method may further include displaying an alert to the user on a userinterface of the monitoring device until the user performs an action ofresponding to the alert. The initial mode may be device-initiatedtransmission, the new mode may be on-demand transmission, the determineddata may be a glucose concentration value, and the transition criteriamay be the glucose concentration value being in a dangerous range for aperiod exceeding a second predetermined duration of time.

In another aspect, the embodiments are directed towards a system forperforming any of the above methods. In another aspect, the embodimentsare directed towards a device or system or method substantially as shownand/or described in the specification and/or drawings.

In another aspect, the embodiments are directed towards an electronicdevice for monitoring data associated with a physiological condition,including: a continuous analyte sensor, where the continuous analytesensor is configured to substantially continuously measure theconcentration of analyte (such as glucose) in the host, and to providecontinuous sensor data indicative of the analyte concentration in thehost; and a processor module configured to perform any one of thedescribed methods.

In another aspect, the embodiments are directed towards an electronicdevice for delivering a medicament to a host, the device including: amedicament delivery device configured to deliver medicament to the host,where the medicament delivery device is operably connected to acontinuous analyte sensor, where the continuous analyte sensor isconfigured to substantially continuously measure the concentration ofanalyte (such as glucose) in the host, and to provide continuous sensordata indicative of the analyte concentration in the host; and aprocessor module configured to perform any one of the described methods.

To ease the understanding of the described features, continuous glucosemonitoring is used as part of the explanations that follow. It will beappreciated that the systems and methods described are applicable toother continuous monitoring systems, e.g., for analysis and monitoringof other analytes, as will be noted in greater detail below.

Any of the features of embodiments of the various aspects disclosed isapplicable to all aspects and embodiments identified. Moreover, any ofthe features of an embodiment is independently combinable, partly orwholly with other embodiments described herein, in any way, e.g., one,two, or three or more embodiments may be combinable in whole or in part.Further, any of the features of an embodiment of the various aspects maybe made optional to other aspects or embodiments. Any aspect orembodiment of a method can be performed by a system or apparatus ofanother aspect or embodiment, and any aspect or embodiment of the systemcan be configured to perform a method of another aspect or embodiment.

Advantages may include, in certain embodiments, one or more of thefollowing. Continuous analyte monitoring may be made more adaptable to agiven situation, requiring less user interaction or input when such isnot required, enhancing usability and user friendliness of a monitoringdevice. Other advantages will be understood from the description thatfollows, including the figures and claims.

Any of the features of embodiments of the various aspects disclosed isapplicable to all aspects and embodiments identified. Moreover, any ofthe features of an embodiment is independently combinable, partly orwholly with other embodiments described herein, in any way, e.g., one,two, or three or more embodiments may be combinable in whole or in part.Further, any of the features of an embodiment of the various aspects maybe made optional to other aspects or embodiments. Any aspect orembodiment of a method can be performed by a system or apparatus ofanother aspect or embodiment, and any aspect or embodiment of the systemcan be configured to perform a method of another aspect or embodiment.

This Summary is provided to introduce a selection of concepts in asimplified form. The concepts are further described in the DetailedDescription section. Elements or steps other than those described inthis Summary are possible, and no element or step is necessarilyrequired. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended foruse as an aid in determining the scope of the claimed subject matter.The claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments now will be discussed in detail with an emphasison highlighting the advantageous features. These embodiments depict thenovel and non-obvious mode switching systems and methods according topresent principles, for use in analyte monitoring and other purposes,shown in the accompanying drawings, which are for illustrative purposesonly. These drawings include the following figures, in which likenumerals indicate like parts:

FIG. 1 is a flowchart according to present principles showing oneimplementation of a general method of mode switching.

FIG. 2 is a diagram according to present principles showing types ofuser interactions.

FIG. 3A-3C are flowcharts according to present principles showing otherimplementations of general methods of mode switching.

FIG. 4 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, based on comparing signalor data usability to one or more criteria such as a threshold.

FIG. 5 is a diagram according to present principles showing aspects ofdata usability.

FIG. 6 is a diagram according to present principles showing aspects ofdata from signal analysis.

FIG. 7 is a diagram according to present principles showing aspects ofdata values per se, or data based on such data values.

FIG. 8 is a diagram according to present principles showing aspects ofother data which may be employed in the determination of data usability,including data from other, e.g., external, devices.

FIG. 9 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingdecision-support aspects such as an intended use of CGM data.

FIG. 10 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingsystem detection of external devices.

FIG. 11 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingtransitions between calibration modes.

FIG. 12 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, again depictingtransitions between calibration modes.

FIG. 13A is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingmultiple modes operating concurrently. FIG. 13B is a chart illustratingmatching of calibration parameters to allow switching.

FIG. 14 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingtransitions between data transmission modes.

FIG. 15A illustrates a progressive sequence of modes, phases, or stages,detailing levels or phases of control within an artificial pancreassystem.

FIG. 15B illustrates a schematic diagram of an artificial pancreassystem.

FIG. 15C is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingtransitions between control modes, e.g., various therapeutic andadjunctive (non-therapeutic) modes, and which may be applicable to themodes shown in FIG. 15A.

FIG. 16A is a flowchart showing mode switching between phases describedin an artificial pancreas system.

FIG. 16B is a diagram showing mode switching between phases described inan artificial pancreas system.

FIG. 16C is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingexertion of varying levels of pump control within therapeutic modes orphases.

FIG. 17 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingresponsive processing and display of data according to mode switchingbetween therapeutic and adjunctive (non-therapeutic) modes.

FIG. 18A-18D illustrate exemplary user interfaces which may be employedaccording to present principles.

FIG. 19 is a chart showing ways to depict data of various confidencelevels.

FIG. 20 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular depictingmultimodal transitions.

FIG. 21A is a diagram depicting ways to display adjunctive(non-therapeutic) mode data and FIG. 21B is a flowchart according topresent principles showing another implementation of a method of modeswitching, in particular depicting transitions tohigher-calibration-required modes caused by user requests for additionalor different data.

FIG. 22 is a flowchart according to present principles showing anotherimplementation of a method of mode switching, in particular identifyingand treating hypoglycemia unawareness.

FIG. 23 is a block diagram that illustrates sensor electronics in oneembodiment.

FIG. 24 is a schematic view of a receiver in one implementation.

FIG. 25 is a block diagram of receiver electronics in one embodiment.

Like reference numerals refer to like elements throughout. Elements arenot to scale unless otherwise noted.

DETAILED DESCRIPTION Definitions

In order to facilitate an understanding of the preferred embodiments, anumber of terms are defined below.

The term “analyte” as used herein generally relates to a substance orchemical constituent in a biological fluid (for example, blood,interstitial fluid, cerebral spinal fluid, lymph fluid or urine) thatcan be analyzed. Analytes can include naturally occurring substances,artificial substances, metabolites, and/or reaction products. In someembodiments, the analyte for measurement by the sensor heads, devices,and methods is analyte. However, other analytes are contemplated aswell, including but not limited to acarboxyprothrombin; acylcarnitine;adenine phosphoribosyl transferase; adenosine deaminase; albumin;alpha-fetoprotein; amino acid profiles (arginine (Krebs cycle),histidine/urocanic acid, homocysteine, phenylalanine/tyrosine,tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers;arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactiveprotein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholicacid; chloroquine; cholesterol; cholinesterase; conjugated 1-βhydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MMisoenzyme; cyclosporin A; d-penicillamine; de-ethylchloroquine;dehydroepiandrosterone sulfate; DNA (acetylator polymorphism, alcoholdehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne/Beckermuscular dystrophy, analyte-6-phosphate dehydrogenase, hemoglobin A,hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F,D-Punjab, beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1,Leber hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax,sexual differentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diptheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I;17-alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids can also constitute analytes in certain embodiments. The analytecan be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte can be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbiturates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body can also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-Dihydroxyphenylacetic acid (DOPAC),Homovanillic acid (HVA), 5-Hydroxytryptamine (5HT), and5-Hydroxyindoleacetic acid (FHIAA).

The term “calibration” as used herein generally relates to the processof determining the relationship between the sensor data and thecorresponding reference data, which can be used to convert sensor datainto meaningful values substantially equivalent to the reference data,with or without utilizing reference data in real time. In someembodiments, namely, in continuous analyte sensors, calibration can beupdated or recalibrated (at the factory, in real time and/orretrospectively) over time as changes in the relationship between thesensor data and reference data occur, for example, due to changes insensitivity, baseline, transport, metabolism, and the like.

The terms “calibrated data” and “calibrated data stream” as used hereingenerally relate to data that has been transformed from its raw state(e.g., digital or analog) to another state using a function, for examplea conversion function, to provide a meaningful value to a user.

The term “algorithm” as used herein generally relates to a computationalprocess (for example, programs) involved in transforming informationfrom one state to another, for example, by using computer processing.

The term “counts” as used herein generally relates to a unit ofmeasurement of a digital signal. In one example, a raw data streammeasured in counts is directly related to a voltage (e.g., converted byan A/D converter), which is directly related to current from the workingelectrode. In another example, counter electrode voltage measured incounts is directly related to a voltage.

The term “sensor” as used herein generally relates to the component orregion of a device by which an analyte can be quantified.

The terms “glucose sensor” and “member for determining the amount ofglucose in a biological sample” as used herein generally relate to anymechanism (e.g., enzymatic or non-enzymatic) by which glucose can bequantified. For example, some embodiments utilize a membrane thatcontains glucose oxidase that catalyzes the conversion of oxygen andglucose to hydrogen peroxide and gluconate, as illustrated by thefollowing chemical reaction:

Glucose+O₂→Gluconate+H₂O₂

Because for each glucose molecule metabolized, there is a proportionalchange in the co-reactant O₂ and the product H₂O₂, one can use anelectrode to monitor the current change in either the co-reactant or theproduct to determine glucose concentration.

The terms “operably connected” and “operably linked” as used hereingenerally relate to one or more components being linked to anothercomponent(s) in a manner that allows transmission of signals between thecomponents. For example, one or more electrodes can be used to detectthe amount of glucose in a sample and convert that information into asignal, e.g., an electrical or electromagnetic signal; the signal canthen be transmitted to an electronic circuit. In this case, theelectrode is “operably linked” to the electronic circuitry. These termsare broad enough to include wireless connectivity.

The terms “in vivo portion” as used herein generally relates to theportion of the device (for example, a sensor) adapted for insertion intoand/or existence within a living body of a host.

The terms “reference analyte monitor,” “reference analyte meter,” and“reference analyte sensor” as used herein generally relate to a devicethat measures a concentration of an analyte and can be used as areference for the continuous analyte sensor, for example aself-monitoring blood glucose meter (SHBG) can be used as a referencefor a continuous glucose sensor for comparison, calibration, and thelike.

The term “system noise” as used herein generally relates to unwantedelectronic or diffusion-related noise which can include Gaussian,motion-related, flicker, kinetic, or other white noise, for example.

The terms “noise,” “noise event(s),” “noise episode(s),” “signalartifact(s),” “signal artifact event(s),” and “signal artifactepisode(s)” as used herein generally relate to signal noise that iscaused by substantially non-glucose related, such as interferingspecies, macro- or micro-motion, ischemia, pH changes, temperaturechanges, pressure, stress, or even unknown sources of mechanical,electrical and/or biochemical noise for example. In some embodiments,signal artifacts are transient and characterized by a higher amplitudethan system noise, and described as “transient non-glucose relatedsignal artifact(s) that have a higher amplitude than system noise.” Insome embodiments, noise is caused by rate-limiting (or rate-increasing)phenomena. In some circumstances, the source of the noise is unknown.

The terms “constant noise” and “constant background” as used hereingenerally relate to the component of the noise signal that remainsrelatively constant over time. In some embodiments, constant noise maybe referred to as “background” or “baseline.” For example, certainelectroactive compounds found in the human body are relatively constantfactors (e.g., baseline of the host's physiology). In somecircumstances, constant background noise can slowly drift over time(e.g., increase or decrease), however this drift need not adverselyaffect the accuracy of a sensor, for example, because a sensor can becalibrated and re-calibrated and/or the drift measured and compensatedfor.

The terms “non-constant noise,” “non-constant background,” “noiseevent(s),” “noise episode(s),” “signal artifact(s),” “signal artifactevent(s),” and “signal artifact episode(s)” as used herein generallyrelate to a component of the background signal that is relativelynon-constant, for example, transient and/or intermittent. For example,certain electroactive compounds, are relatively non-constant due to thehost's ingestion, metabolism, wound healing, and other mechanical,chemical and/or biochemical factors), which create intermittent (e.g.,non-constant) “noise” on the sensor signal that can be difficult to“calibrate out” using a standard calibration equations (e.g., becausethe background of the signal does not remain constant).

The terms “low noise” as used herein generally relates to noise thatsubstantially decreases signal amplitude.

The terms “high noise” and “high spikes” as used herein generally relateto noise that substantially increases signal amplitude.

The term “variation” as used herein generally relates to a divergence oramount of change from a point, line, or set of data. In one embodiment,estimated analyte values can have a variation including a range ofvalues outside of the estimated analyte values that represent a range ofpossibilities based on known physiological patterns, for example.

The terms “physiological parameters” and “physiological boundaries” asused herein generally relate to the parameters obtained from continuousstudies of physiological data in humans and/or animals. For example, amaximal sustained rate of change of glucose in humans of about 4 to 5mg/dL/min and a maximum acceleration of the rate of change of about 0.1to 0.2 mg/dL/min² are deemed physiologically feasible limits; valuesoutside of these limits would be considered non-physiological. Asanother example, the rate of change of glucose is lowest at the maximaand minima of the daily glucose range, which are the areas of greatestrisk in patient treatment, thus a physiologically feasible rate ofchange can be set at the maxima and minima based on continuous studiesof glucose data. As a further example, it has been observed that thebest solution for the shape of the curve at any point along glucosesignal data stream over a certain time period (for example, about 20 to30 minutes) is a straight line, which can be used to set physiologicallimits. These terms are broad enough to include physiological parametersfor any analyte.

The term “measured analyte values” as used herein generally relates toan analyte value or set of analyte values for a time period for whichanalyte data has been measured by an analyte sensor. The term is broadenough to include data from the analyte sensor before or after dataprocessing in the sensor and/or receiver (for example, data smoothing,calibration, and the like).

The term “estimated analyte values” as used herein generally relates toan analyte value or set of analyte values, which have beenalgorithmically extrapolated from measured analyte values.

As employed herein, the following abbreviations apply: Eq and Eqs(equivalents); mEq (milliequivalents); M (molar); mM (millimolar) μM(micromolar); N (Normal); mol (moles); mmol (millimoles); μmol(micromoles); nmol (nanomoles); g (grams); mg (milligrams); μg(micrograms); Kg (kilograms); L (liters); mL (milliliters); dL(deciliters); μL (microliters); cm (centimeters); mm (millimeters); μm(micrometers); nm (nanometers); h and hr (hours); min. (minutes); s andsec. (seconds); ° C. (degrees Centigrade).

The phrase “continuous glucose sensor” as used herein generally relatesto a device that continuously or continually measures the glucoseconcentration of a bodily fluid (e.g., blood, plasma, interstitial fluidand the like), for example, at time intervals ranging from fractions ofa second up to, for example, 1, 2, or 5 minutes, or longer.

The phrases “continuous glucose sensing” or “continuous glucosemonitoring” as used herein generally relate to the period in whichmonitoring of the glucose concentration of a host's bodily fluid (e.g.,blood, serum, plasma, extracellular fluid, tears etc.) is continuouslyor continually performed, for example, at time intervals ranging fromfractions of a second up to, for example, 1, 2, or 5 minutes, or longer.In one exemplary embodiment, the glucose concentration of a host'sextracellular fluid is measured every 1, 2, 5, 10, 20, 30, 40, 50 or 60seconds.

The term “substantially” as used herein generally relates to beinglargely but not necessarily wholly that which is specified, which mayinclude an amount greater than 50 percent, an amount greater than 60percent, an amount greater than 70 percent, an amount greater than 80percent, an amount greater than 90 percent, or more.

The terms “processor” and “processor module,” as used herein generallyrelate to a computer system, state machine, processor, or the like,designed to perform arithmetic or logic operations using logic circuitrythat responds to and processes the basic instructions that drive acomputer. In some embodiments, the terms can include ROM and/or RAMassociated therewith.

The terms “usability”, “data usability”, and “signal usability”, as usedherein generally relate to accuracy, reliability, and/or confidence insensor data. In some cases such terms may also relate to stability ofthe sensor data.

Exemplary embodiments disclosed herein relate to the use of a glucosesensor that measures a concentration of glucose or a substanceindicative of the concentration or presence of another analyte. In someembodiments, the glucose sensor is a continuous device, for example asubcutaneous, transdermal, transcutaneous, non-invasive, intraocularand/or intravascular (e.g., intravenous) device. In some embodiments,the device can analyze a plurality of intermittent blood samples. Theglucose sensor can use any method of glucose measurement, includingenzymatic, chemical, physical, electrochemical, optical, optochemical,fluorescence-based, spectrophotometric, spectroscopic (e.g., opticalabsorption spectroscopy, Raman spectroscopy, etc.), polarimetric,calorimetric, iontophoretic, radiometric, and the like.

The glucose sensor can use any known detection method, includinginvasive, minimally invasive, and non-invasive sensing techniques, toprovide a data stream indicative of the concentration of the analyte ina host. The data stream is typically a raw data signal that is used toprovide a useful value of the analyte to a user, such as a patient orhealth care professional (e.g., doctor), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor capable of measuring the concentration of glucose in a host, thesystems and methods of embodiments can be applied to any measurableanalyte. Some exemplary embodiments described below utilize animplantable glucose sensor. However, it should be understood that thedevices and methods described herein can be applied to any devicecapable of detecting a concentration of analyte and providing an outputsignal that represents the concentration of the analyte.

In some embodiments, the analyte sensor is an implantable glucosesensor, such as described with reference to U.S. Pat. No. 6,001,067 andU.S. Pat. No. 8,828,201. In some embodiments, the analyte sensor is atranscutaneous glucose sensor, such as described with reference to U.S.Pat. No. 7,497,827. In yet other embodiments, the analyte sensor is adual electrode analyte sensor, such as described with reference to U.S.Pat. No. 8,478,377. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Pat. No. 7,460,898. These patents and publications areincorporated herein by reference in their entirety.

The following description and examples describe the present embodimentswith reference to the drawings. In the drawings, reference numbers labelelements of the present embodiments. These reference numbers arereproduced below in connection with the discussion of the correspondingdrawing features.

In general, systems and methods according to present principles relateto real time switching between a first or initial mode of userinteraction and a second or new mode of user interaction. In some cases,users may confirm such switching before it occurs, or may be notifiedabout the same. In many cases, however, the switching will be automaticand transparent to the user. The mode switching may cause a switch froman initial mode to a new mode, followed by a switch to a subsequent modeor back to the initial mode. In any case, the mode switching willgenerally affect the user's interaction with the device, and not justcause internal processing changes within the device, although suchprocessing changes will generally accompany the mode switching. Suchuser interaction with the device may generally affect how the usercalibrates the monitoring device, views and uses the data, or the like.The mode switching may generally have a significant effect on the userinteraction as embodied in the user interface, including aspects fromboth the input and the output. B

In many cases the decision or trigger to switch between modes relates toa usability of the monitoring device which is in many cases (but notalways) related to the usability of a sensor signal, as compared to atransition criteria, but may also be based on other data.

For example, referring to the flowchart 100 of FIG. 1, a monitoringdevice may start in a first, original, or initial mode of operation oruser interaction 102. Upon the occurrence of a trigger 106, themonitoring device may switch to a second or new mode of operation orinteraction 104. The trigger 106 generally comes about when one or moretrigger criteria are met, e.g., when one or more values of determineddata meets, or is predicted to meet, one or more respectivepredetermined threshold transition criteria. One type of criterion usedin some implementations is data or signal usability, but other data andaccompanying criteria are also often used.

As one example, an analyte monitoring device may transition or switchfrom a user-dependent calibration mode to a device self-calibrationmode, i.e., from a calibration routine using blood glucose concentrationvalues from an external meter to a calibration routine performed by thedevice it without requiring a user to provide a glucose value from,e.g., a self-monitoring blood glucose meter. Device self-calibrationgenerally requires one or more of stringent manufacturing controls,internal measurements, entry of calibration codes from the manufacturer(which themselves constitute a priori information) and/or associatedalgorithms that enable calibration of the device without externalmeasurement obtained during sensor wear.

Additional details of systems and methods for device self-calibrationare disclosed in U.S. patent application Ser. No. 13/827,119; and USPatent Publication No. 2012/0265035-A1 both of which are owned by theassignee of the present application and herein incorporated by referencein their entireties.

The converse transition or switch may also be performed. As anotherexample, an analyte monitoring device may transition or switch fromproviding information or data on-demand to a user to a mode in whichinformation or data is provided as initiated by the device, e.g., as aregular or irregular periodic communication. The converse transition orswitch may also be performed. As yet another example, an analytemonitoring device may transition or switch from providing information ordata in one decision-support mode to another, e.g., from a therapeutic(or non-adjunctive) use to an adjunctive (or non-therapeutic) use orfrom one phase of control to another. Again, the converse transition orswitch may also be performed, as well as transitions or switches toother modes.

A number of triggers will be described, and generally the triggers aresuch that one or more criteria are met or satisfied. In one common typeof criteria, a determined parameter or variable meets or exceeds apredetermined threshold, or is determined to do so in the future. Thedetermined parameter or variable may be data associated with the sensorsignal, i.e., the sensor signal value or a scaled representativethereof, data about the sensor signal, e.g., data indicating its noiselevel or the like, data from an external source, e.g., data from a bloodglucose meter, temperature sensor, clock, location sensor, or the like,as well as other data as will be described below.

Systems and methods according to present principles may use one or manytriggers, i.e., transition criteria, on which to base mode switching fora given implementation. In some cases, for specific types of modeswitching, particular determined parameters or variables will beespecially useful. For example, signal usability may be especiallypertinent when deciding and switching calibration modes ordecision-support modes. The signal value itself may be especiallypertinent when deciding data transmission modes, e.g., whether a largeexcursion has occurred. However, these are purely exemplary and it willbe understood that in given implementations other criteria will proveuseful. It will further be understood that in any given implementationany number or type of transition criteria may be employed, including insome cases a single criterion.

FIG. 2 shows a diagram 125 illustrating a number of types of userinteractions 108, which may be identified with modes of userinteraction. For example, and as noted above, one variety of userinteractions 108 which may be subject to mode switching involves acalibration mode 110. Calibration modes may include user-dependentcalibration, device self-calibration, and so on. The calibration modemay be in part dependent on data usability, as well as on other data.

Another type of user interaction 108 involves the use to which the userwill put the data, such as the use of the data in a decision-supportmode 114. Exemplary uses include therapeutic, where the monitoringdevice is used in the calculation and/or to direct delivery of insulin,or adjunctive, where the monitoring device is used for information butwhere insulin dosing is based on external user calculations and/or aconfirmatory external meter value, e.g., from a blood glucose meter. Ifthe use is directly related to patient health, e.g., therapeutic, thenthe requirements of the signal will generally be higher than if not.Thus, lower signal usabilities (less reliable data) may result intransitions from therapeutic to adjunctive modes. Some decision-supportmodes include modes used only for information or augmentation ofeducation, e.g., tracking and trending, as well as (on the otherextreme) “closed-loop” modes in which data from the monitoring device isdirectly used to drive a medicament delivery pump.

In the context of Type I diabetes, the terms “non-therapeutic” and“adjunctive” may be used interchangeably or as synonyms, similarly theterms “therapeutic” and “non-adjunctive” may also be usedinterchangeably or as synonyms. The terms therapeutic (non-adjunctive)and adjunctive (non-therapeutic) are used in a relative sense, and itshould be noted that the terms may have different connotations for otherhealth indication that may be dependent upon populations and/ortechnology. For example, for a type I user, a therapeutic ornon-adjunctive mode may mean one in which the user receives dosingrecommendations, while an adjunctive or non-therapeutic mode may referto one in which recommendations are given but the same do not relate toinsulin dosing. The descriptions herein may be primarily directedtowards a usage of the terms “therapeutic” and “adjunctive” aspertaining to delivery of therapy such as insulin dosing for type Idiabetics. However, the same terms or similar may be employed for otherpopulations. For example, for type II users or for those interested inhealth or fitness optimization, a therapeutic use may be one in which ameal or exercise is suggested, while an adjunctive use is one in whichinformation is delivered without a specific recommendation associatedtherewith.

In health indications other than Type I diabetes, the terms“non-therapeutic” and “adjunctive” have meanings that are overlapping,but not necessarily identical; similarly, the terms “therapeutic” and“non-adjunctive” have meanings that are overlapping, but not necessarilyidentical. In the example of a type II user, or a user with a generalinterest in food or exercise optimization, a therapeutic mode may meanone in which the user receives food and exercise recommendations, whilean non-therapeutic mode may refer to one in which information isprovided to the user, without any specific recommendations. For example,such a user may include those with interests in optimizing sports orfitness routines or eating habits, users interested in losing weight orotherwise increasing their health, or indeed any other user interestedin bettering their health or learning more about how their habits andactions affect their health. For these users, therapeuticrecommendations may include suggested meals, foods, or recipes, based ondata known by the system including glucose value, glucose rate ofchange, activity level, sleeping patterns, and the like. For usersparticularly interested in fitness optimization, data may be employedsuch as activity data, such as may be received from an accelerometer orGPS device. Such optimization routines may also include data gained fromCGM including glucose, glucose rate of change, and the like.Informational or adjunctive data may be provided, e.g., total caloriesburned that day, and so on.

Additional details about type II user recommendations may be found inco-pending U.S. Provisional Patent Application Ser. No. 62/158,463,owned by the assignee of the present application and herein incorporatedby reference in its entirety.

More generally, an analyte monitoring device may be configured to switchbetween different modes or phases of an artificial pancreas system asillustrated in FIG. 15A.

Yet another type of user interaction involves the mode 110 in whichtransmission of the data from the sensor electronics to the monitoringdevice occurs. If data is pulled on-demand by the monitoring device fromthe sensor or accompanying sensor electronics, then such is termed anon-demand mode, while if the data is pushed periodically or uponoccurrence of an event, such is termed a device self-initiated mode.

Yet another type of user interaction mode is a confirmation mode 112.This mode may be considered separately from the modes above, but oftenplays a role in the implementation of one of the other modes. In thismode, a user is prompted by a confirmatory prompt or query prior to amode switch or in some cases prior to a change within a particular mode.For example, prior to switching from device self-calibration mode touser-dependent calibration mode, the user may be prompted to confirmthat they understand that finger sticks will now be required.

The flowchart 150 of FIG. 3A indicates a general method for modeswitching. In a first step, the monitoring device is operating in aninitial mode (step 117). The monitoring device may have startedfollowing power up in the initial mode, or may have switched to the modeusing systems and methods similar to those disclosed here. A next stepis that data is received or otherwise determined (step 118). The datathat is received or otherwise determined as discussed in greater detailbelow, but generally relates to a data signal from the sensor, a dataquality parameter associated with the data signal from the sensor usingsignal analysis, external data, or the like. A next step is to check ifthe data received or determined is such that that a criterion for modeswitching is met (step 120). In other words, the received or determineddata is checked to see if the same should cause a switch of modes, e.g.,whether it meets, matches, or satisfies certain criteria, e.g., meets orexceeds a threshold for a transition criteria.

Of course, it will be understood that different transition criteria maybe satisfied or met by different determined data in any given situation,and the satisfaction of a criteria for one type of transition may beaccompanied by other concurrent or overlapping mode switching steps.

This situation is illustrated in FIG. 3B, in which an initial mode 137may be caused to transition to a first new mode 141 if a firsttransition criterion 139 is met by a determined parameter or variable,and in the same way the initial mode 137 may be caused to transition toa second new mode 145 if a second transition criterion 143 is met, andso on for transitions to modes 149 and 153 caused by the meeting oftransition criteria 147 and 151, respectively. For example, a device mayswitch to a user dependent calibration mode and may simultaneously (ornot) transition to a therapeutic mode. Exemplary such multimodaltransitions are described in greater detail below.

The meeting of multiple transition criteria may result in a differentmode being transitioned to as compared to the case where just onetransition criterion is met. This situation is illustrated in theflowchart 155 of FIG. 3C. An initial mode 157 can transition to a newmode 161 upon the meeting of a first transition criterion 159 (solidlines). However, an initial mode 157 can also transition to a second newmode 165 upon the meeting of both a first transition criterion 159 and asecond transition criterion 163 (dotted lines).

As noted above, in many cases a “determined data” that is compared to atransition criterion will relate to data or signal usability. In aspecific method using such, as illustrated by the flowchart 175 of FIG.4, in a first step, a first level of signal usability is determined(step 128). It is assumed that the first level of signal usability doesnot result in a switch or transition of modes. A subsequentdetermination is made of a level of signal usability (step 130), and ifthe subsequent level of signal usability is sufficiently different fromthe first level (step 132), i.e., satisfies a transition criteria, e.g.,is greater than or equal to a threshold level of difference, then themode may be changed from the initial mode to a subsequent mode (step134). That is, the mode may switch depending on whether the evaluationdetermined that the criteria (or criterion) for mode switching was met.

If the subsequent level of signal usability is not sufficientlydifferent, then the cycle may begin again. In a related implementation,the signal usability need not be measured against a prior measuredlevel, but may simply be compared to a criterion based on an absolutestandard. If the signal usability varies from the absolute standard bygreater than a predetermined threshold amount, the transition to the newmode may again occur.

In any case, data is output on a user interface based on the modedetermined, e.g., either the initial mode or the new mode. In someimplementations it may be useful to indicate to the user how long a newmode is expected to last, e.g., whether the same is transient orlong-term. As an example, if a decision-support mode switches fromtherapeutic to adjunctive, due to a sensor fault that is transient,e.g., a dip and recover fault, the user may be informed that theadjunctive use is temporary and should last for less than about one day.As another example, in calibration modes, if there is full confidence inthe device self-calibration, then the device may indicate that noadditional blood glucose meter values may be required for the remainderof the sensor session. Similarly, in addition to selectively outputtingbased on the mode, and providing an indication of the mode, the UI maydisplay a performance indicator for how well the sensor is performing.

Specific triggering or transition criteria are now described, and it isnoted that such may be applied to certain or all embodiments notedherein. The criteria may be used individually, as part of a single-modeor single-variable transition, or multiple criteria and data may bemonitored and compared simultaneously or at nearly the same time,enabling multimodal transitions, e.g., causing a change in calibrationtype and a change in decision support mode. The embodiments describedherein in which the below criteria may be employed include, e.g., thegeneral transitions noted in FIGS. 1, 3A, 3B, 3C, and 10, thetransitions noted in FIGS. 2, 9, 11-17, 20 and 21A, the specific use ofcriteria related to data usability in FIG. 4, including the use ofthresholds, and the criteria may be applied to the specific types ofdata described in FIGS. 5-8, with results as displayed in FIGS. 18A-18Dand 24.

Referring to the diagram 200 of FIG. 5, a parameter or variable 136relating to the data usability may be considered to occupy one or moreof a number of categories. The parameter or variable 136 may also bebased on a combination of contributions from these categories, e.g., alinear or nonlinear combination, and in some implementations withweightings applied.

One category of data on which the data usability 136 may be based is onthe data values per se 138. This category generally relates to sensorsignal values as well as scaled or normalized representations of thesevalues, e.g., where the first is measured in an analog or digital value(e.g., counts) and the second is often measured by a concentration suchas mg/dL. It may also relate to data based on such values, e.g., timerates of change, determined patterns, historical values, excursions, andthe like. In some cases fault data may be used in the calculation ofdata usability as the same may be based on data values per se.

Another category of data on which the data usability 136 may be based ison data signal analysis 140. The data signal analysis 140 pertains todata about the received signal, e.g., noise levels, variability, in somecases discriminated faults, frequency analysis, and other inputs such asthose related to data quality.

Another category of data on which the data usability 136 may be based ison a decision-support mode 144. As noted above, such modes relate touses to which users and/or clinicians put the data, and may includemodes such as therapeutic, adjunctive, or different phases of control ofan artificial pancreas system as illustrated in FIG. 15A, and the like.

A further category of data on which the data usability 136 may be basedis on other external data 142. This category includes data from sourcesother than the sensor or the decision-support mode. Such data mayinclude user-entered data, e.g., about how the user is feeling, mealsingested, exercise performed, and the like, as well as data from otherexternal devices, e.g., blood glucose monitors, temperature sensors, andthe like. Certain of these external devices may be signally coupled orconnected to the monitoring device, and others may be employed by havingthe user enter data from the external device into the monitoring device.

It will be understood that certain factors may be considered to occupymore than one of these categories. It will be further understood thatother categories of data may also be employed. Finally, it is noted thata particular mode transition may be, and often is, based on multiplesources of data from multiple respective categories. Each of thesefactors and categories of data is described further below with respectto subsequent diagrams.

Data Signal Aanalysis

FIG. 6 is a diagram 225 illustrating exemplary types of data 140 fromsignal analysis. In most of these types, sensor signal data is analyzedresulting in data 140 that indicates in some way the usability of thesensor signal data, e.g., its reliability, accuracy, stability,confidence, or other aspects as described or understood. In certainother cases, data 140 does not relate to usability, but may still beemployed in the determination of mode switching.

In one specific example, the signal analysis data 140 may include data164 about noise in the signal. That is, if the signal has a noise levelwhich satisfies a criteria, e.g., is above a predetermined threshold,the usability of the signal or resulting data may be deemed, calculated,or determined to be lower than if the noise level were below thepredetermined threshold. Accordingly, in high noise situations, certainmodes, such as a therapeutic decision-support mode, may be caused totransition to an adjunctive mode, as the therapeutic reliability of thesignal is in question. Alternatively, where the therapeutic level ofcontrol is enumerated by phases, the same may be caused to transition toa lower phase. Noise level determination may also be made over the shortterm or the long term, and in some cases can be determined by analysisof the signal value itself over the long-term or short-term. Forexample, a long-term drift in a signal baseline value may be indicativeof certain types of faults as described in the applications incorporatedby reference below.

The signal analysis data 140 may also be based on data 166 about faultsdetected or discriminated in the signal. Such faults include, e.g., endof life faults, compression faults, dip and recover faults, water spikefaults, and the like. In many cases the analysis of such faults includescomparing signal patterns or certain criteria known to be associatedwith such faults. In this sense the data 140 may be considered to dependto a certain extent on signal values per se. In some cases, where asignificant such fault is discriminated, a transition may be causedfrom, e.g., a device self-calibration mode to a user-dependentcalibration mode.

Additional details of systems and methods for noise and fault detectionand discrimination are disclosed in U.S. Pat. No. 8,010,174; US PatentPublication No. 2009/0192366-A1, or U.S. Pat. No. 8,290,559; and USPatent Application No. 62/009,065 which are owned by the assignee of thepresent application and herein incorporated by reference in theirentireties.

The signal analysis data 140 may also be based on data 158 about apatient's previous sensor session. For example, in some cases aparticular patient may consistently experience a long startup time fortheir sensor sessions. In these cases, it may be expected that modesrelying on external data may be determined to be more reliable duringsuch times. Accordingly, those modes may be switched to from othermodes. As a specific example, user-dependent calibration may be reliedon more than device self-calibration during these times.

The signal analysis data 140 may also be based on data 160 about errorsdetermined at calibration. Such errors at calibration are related to asystematic bias between a sensor signal value and a reference externalmeter value. For example, if a significant error at calibration isdetermined, a mode may be caused to switch from a therapeutic one to anadjunctive one, because of a lessened reliability or usability of thesignal. Additional details of systems and methods for determination ofsuch errors at calibration are disclosed in US Patent Publication No.2014/0182350-A1 which is owned by the assignee of the presentapplication and herein incorporated by reference in its entirety.

The signal analysis data 140 may also be based on data 148 about or fromvarious diagnostic routines, including self-diagnostics routines 152 andcalibration-diagnostics routines 154. The former may include periodicdiagnostic routines run by the monitoring device and/or sensorelectronics to detect errors introduced during routine operation. Thelatter relate to routines involved with device self-calibration. Theresults of calibration diagnostics may include the determination andusage, e.g., in transition criteria, of a confidence interval indisplayed readings based on the determined calibration and, inparticular, its quality. For example, if calibration-diagnosticsroutines indicate that the device is not well-calibrated by a deviceself-calibration routine (e.g., low confidence level), the mode may becaused to switch to user-dependent calibration. In some cases, themonitoring device may periodically enter a self-diagnostics mode to,e.g., cause an interrogation of the sensor, as well as to cause anexamination of the resulting transient response. If the response isexpected or consistent, mode switching may be contraindicated. If theresponse is unusual or indicates a problem with the sensor, then modeswitching may be indicated, e.g., a switch to a more conservative,safer, or user-protective mode, even if the same results in lesseneduser convenience.

Additional details of systems and methods for determination of data fromdiagnostic routines are disclosed in US Patent Publication No.2012/0265035-A1, which is owned by the assignee of the presentapplication and herein incorporated by reference in its entirety.

The signal analysis data 140 may also be based on data 162 from otheralgorithms, e.g., which estimate errors in determined slopes and/orbaselines corresponding to rate of change data. In some cases, wherethese are statistically equivalent between modes, such may be anindicator that a mode switch is possible. Additional details of suchaspects are described below with respect to FIG. 13 and elsewhere

Other types and aspects of signal analysis data are disclosed in U.S.Pat. No. 8,010,174; US Patent Publication No. 2009/0192366-A1; or U.S.Pat. No. 8,290,559; US Patent Publication No. 2012/0265035-A1; U.S.patent application Ser. No. 13/827,119; U.S. patent Publication No.2014/0182350-Al; US Patent Application No. 61/978,151; and US Patentapplication Ser. No. 009,065, which are owned by the assignee of thepresent application and herein incorporated by reference in theirentireties.

Data Values Per Se

FIG. 7 is a diagram 250 illustrating exemplary types of analysis 138pertaining to data values per se.

The analysis of data value 138 may be based on data 182 directly relatedto the glucose concentration value itself, or related to values uniquelydetermined by the glucose concentration value, e.g., a glucose rate ofchange, including slope and/or acceleration, or glycemic state, e.g.,hypoglycemia, hyperglycemia, or euglycemia. A particular example of theuse of such data is given by analysis of large excursions 170, in whichif a large excursions from a desired value or range is encountered, themode is caused to switch so as to provide additional safety for a user.For example, if a large excursion is seen (e.g., glucose value drop from150 to 50 within an hour), and the monitoring device is in a deviceself-calibration mode, the same may be switched to a user-dependentcalibration in order to obtain additional and external data, outside ofthe context of the monitoring device itself. Such data 178 may furtherbe based on a glucose or glycemic zone, e.g., a range of glucoseconcentration values. For example, a mode may be caused to switch in ahypoglycemic zone but not in a hyperglycemic one.

In a similar way, the data 138 may also be based on data 172 of durationwithin a range or zone of glucose concentration values, or even within arange or zone of a particular rate of change or acceleration. Forexample, mode switching, which in a given implementation is partiallybased on a glycemic range, may be limited to situations in which theglycemic range is maintained for greater than a predetermined thresholdperiod of time. In this way, mode switching is avoided where a glycemicrange is simply passed through or only briefly encountered.

The data 138 may also be based on data 178 of a glycemic urgency index,which is a quantity determined by analysis of various parameters orvariables, and which relates to not just a glycemic value but moregenerally to an indication of a patient or user's risk state. Forexample, a glycemic urgency index may be employed to determine not justif a patient is hyperglycemic, but to further quantify a measure of riskdue to a patient's hyperglycemia. As applied to mode switching, if apatient has a high glycemic urgency index, the mode may switch to a moreconservative or protective one, to ensure patient safety during the timeof the high value.

Additional details of systems and methods for determining in using aglycemic urgency index are disclosed in U.S. Patent Application No.61/978,151, which is owned by the assignee of the present applicationand herein incorporated by reference in its entirety.

The data 138 may include data 186 based on comparisons to historicalglucose signals and patterns which may be determined or identifiedtherefrom. In these comparisons, current sensor values and patterns maybe compared to prior or historic values and patterns, and similaritiesand deviations determined. As a specific example, if glucose patternsdiverge from a normal glucose patterns for a particular host by aparticular amount, then adjunctive mode may be triggered.

The data 138 may also be based on data 188 about data trends, which mayinclude short-term data trends 190 and long-term data trends 192.Certain aspects of trend data are discussed above in context with datasignal analysis, but here it is noted that overall trends in data may benoted and taken account of in calculations or determinations for modeswitching. For example, if an overall trend is seen for a signalreduction in amplitude, e.g., due to the sensor saturation or sensorlack of sensitivity, then such may be noted and in some cases used asthe basis for causing mode switching. Of course, in some cases, thesensitivity may be reduced so much that the usability of the signal isno longer appropriate for a particular mode, and thus the mode must beswitched to one in which the signal usability is appropriate and withina specified confidence level.

The data 138 may also be based on data 174 based on rebound variability.Rebounding is a phenomenon that occurs when a high blood glucose leveloccurs directly after and in response to a low blood glucose level.Rebounding may occur when a user ingests a significant amount ofcarbohydrates to address a hypoglycemia situation. As rebounding is aknown phenomenon, its occurrence, when consistent, may not necessarilyresult in a mode switch or transition. However, when rebounding occursvariably and not consistently, then the usability of the data may beplaced into question. In such cases, mode switching may be anappropriate response. For example, a therapeutic mode may transitionfrom one phase to another of control or to an adjunctive mode, a deviceself-calibration mode may transition to a user-dependent calibrationmode, and the like.

The data 138 may also be based on data 194 about glucose context, e.g.,whether the data concerns nighttime hypoglycemia. Additional details ofsystems and methods for determination of glucose context are disclosedin U.S. Patent Application No. 61/978,151, incorporated by referenceabove, and applications incorporated by reference therein.

The data 138 may also be based on data 196 entered by a user, e.g.,where a user is prompted to enter such or where the user enters such ontheir own. For example, a user may be prompted to enter whether theybelieve data to be unusually high or low, whether an inaccuracy isperceived, potential causes, or the like. Such user-entered data isfurther described below with respect to FIGS. 8, 10, 11, 12, and 15.

The data 138 may also be based on data 176 received from other sensors,particularly where there are concurrent or overlapping sessions, i.e.,with two sensors simultaneously installed in a patient. In such systems,the data may be compared or shared. A related type of data is comparisondata or difference data, which is a difference value or other comparisonindicator between the data 176 and that from a subject or main sensorsignal.

The data 138 may also be based on data 184 which represents a historicaldata value from a previously-installed sensor. For example, a last setof data values from and immediately—prior sensor may be compared withthe first set of data values from a newly—installed sensor to determineif, e.g., the sensor signal trajectory is consistent. If not, and if thehistorical data values had a high confidence level, then the usabilityof the signal from the new sensor may be lowered.

Other Data Including External Data

FIG. 8 is a diagram 275 illustrating exemplary types of external data142 corresponding to non-sensor signal data, including data such as fromconnected (or not connected) devices.

As a first example, the external data 142 may be metadata 224. Forexample, metadata may include information about a particular lot fromwhich a group of sensors is drawn. Such information is employed onoccasion for purposes of identifying products for various reasons.Metadata analysis may be employed in the determination of transitioncriteria using such data because metadata may be used to identify likesensors which behave and operate identically or similarly to othersensors. For example, if given certain conditions a given sensortransitions from one mode to another, it may be expected that undersimilar conditions a sensor from the same lot (as identified bymetadata) may act similarly. In other words, historical data based onprior sensors may be extrapolated for use in later sensors, either foran individual, for a group, or for a manufactured lot. Where data iscompared across a group of users or a lot, cloud or other networktechnology may be employed to transmit such metadata or otherinformation.

The external data 142 may also include sensor environment data 168. Itis noted that sensor environment data may also be to some extentidentified and quantified by data 140 from signal analysis or data 138pertaining to data values per se. The sensor environment data 168 mayinclude such information as temperature or altitude, as sensors may actdifferently in different environments. For example, a mode which may beswitched to on the basis of comparing a determined data value to acriterion may not cause a transition in another situation, e.g., becausethe change in value of the data may be determined to be based onaltitude rather than a change in a user physiological parameter. Thereception of such external data may be via a user input on a userinterface of the monitoring device, by automatic detection via aconnected device, or by other means. For example, a temperature sensormay be disposed along with the analyte sensor to determine temperature(data 254) and to transmit the same to the monitoring device. As anotherexample, in the determination and use of altitude in a calculation, analtimeter or GPS device may be employed, the latter being available inmany mobile devices. Data transmission may occur via wired or wireless,e.g., Bluetooth®, NFC, or other like techniques.

More generally, the external data 142 may be based on data 222 fromother connected devices. Other connected devices may include, e.g.,activity monitors, sleep monitors, insulin or other medicament deliverypumps, insulin pens, e.g., smart pens, or the like. Other connecteddevices may also include devices not specific to medical purposes, e.g.,GPS devices or mobile devices running GPS location applications, and soon. Other connected devices may further include data from other types ofsensors disposed locally with a main analyte sensor. For example,impedance readings may be determined from the sensor data or along withthe sensor data, and such may bear on the usability of a signal as suchmay be employed in the determination of sensor sensitivity. Auxiliary orredundant sensors may also be so used.

For example, an activity monitor or a sleep monitor may be employed todetermine a status of a user. In a specific implementation andsituation, a CGM device may detect hypoglycemia in a user. If CGM deviceis operating in an on-demand data transmission mode, and if data from anactivity monitor or sleep monitor indicates that the patient issleeping, then the mode may switch to a “pushed” mode of datatransmission to pro-actively alert the user of a potential “overnightlow”, which is generally a very dangerous condition.

The external data 142 may be as noted based on data 220 about locationfrom, e.g., GPS or other location data sources. For example, a user maydesire that a CGM is in a first mode at home and a second mode at workor school. In a specific implementation, a user may desire that datatransmission may be on-demand at work or school and in a periodic ordevice-initiated transmission mode at home. Other variations will alsobe understood.

The external data 142 may further be based on data from other (e.g.,glucose) sensors, which can serve as a check on the reliability of asubject monitoring device. For example, if other devices show a glucoseconcentration value that greatly differs from the subject monitoringdevice, such may call into question the main sensor or the usability ofthe data from the subject monitoring device. Conversely, if otherdevices show an analyte concentration value consistent with the subjectmonitoring device, such may increase the confidence level of the glucoseconcentration value from the subject monitoring device.

The external data 142 may also be based on data 214 about a frequency ofcalibration. Such frequency of calibration data may be determined by themonitoring device, based on the number of entries provided by the useras calibration measurements. In an example, the entering of morecalibration entries than usual may indicate that the user fails to trustthe CGM device, for one reason or another. This increase in frequency ofcalibration may be particularly evident of a low signal usability whenthe difference between the CGM value and the blood glucose concentrationvalue differ significantly, e.g., more than about 20 mg/dL or 20%. Onthe other hand, in some cases, an increase in frequency is associatedwith greater usability as additional data values are received.

The external data 142 may also include data 221 about the tissue inwhich the sensor is located, and other tissue/sensor interface data.Such data may include characterization of tissue type, wound response,diffusion distance, temperature as determined independently or bytemperature data 254, oxygen depletion rate, as well as related sensordata such as thickness of membrane over electrode array, a type ofmembrane, diffusion of specific species between the electrodes, and thelike. In some cases certain of these parameters may be determined byimpedance data 258, which may be determined by data signal analysis aswell. The above data may be entered by the user or identified bymetadata analysis, or obtained via other means.

The external data 142 may also be based on data 248 of the time sinceimplant. In particular, data about the time since implantation of thesensor may bear strongly on the determination and discrimination ofcertain faults such as those associated with “dip and recover” orend-of-life phenomena. By analyzing the time since implant, systems andmethods according to present principles may discriminate such faults, ordetermine such faults to have not occurred, with greater confidence. Inuse, within a system and method according to present principles, databased on analysis 248 may, e.g., determine that a “dip and recover”fault is likely, and as a consequence cause a transition from atherapeutic mode to an adjunctive mode. Confidence in the discriminationof such a fault may be even further enhanced by data signal analysisand/or data value analysis.

Additional details of systems and methods for determining anddiscriminating faults and failure modes are disclosed in U.S. PatentApplication No. 62/009,065, which is owned by the assignee of thepresent application and herein incorporated by reference in itsentirety.

The external data 142 may also be based on data 226 of user-suppliedinformation, e.g., user responses to queries or prompts on the userinterface of the monitoring device. For example, a user may bequestioned as to whether an adhesive on the sensorelectronics/transmitter appears to be peeling. If the user responds inthe affirmative, the result may be less confidence in the sensor readingand thus reduced sensor signal usability. The data 226 can also relateto user selection of responsiveness 240, e.g., whether a user preferssmooth data with a time lag or noisy data that is more responsive toactual glucose changes. Such a selection may be entered on a userinterface that, e.g., appears as a slider switch on the user interfacewhere the user slides their “responsiveness” preference.

The data 226 may further include data 246 where a user flags outlierdata, e.g., where the patient flags data that is unusually high or low,or that represents false alarms, e.g., where the patient identifieswhere the CGM is showing highs or lows incorrectly, as well as userfeedback 244 about alerts or alarms. Data 242 about responsive actionsmay also be included in data 226. For example, if the user takes noactions or responses to alerts or alarms, then it may be inferred thatthe user is not a reliable source of data. These aspects are describedin greater detail below in the context of specific mode transitions.

The data 226 of user-entered or user-supplied information may furtherinclude information in the form of user-entered data 230. The data 230can include data 232 about user perceptions, e.g., whether CGM dataappears accurate, or whether it appears high or low. Such data can alsoinclude user perceptions about glycemic ranges, e.g., whether the userfeels like they have low or high blood sugar. The data 230 can alsoinclude data based on user input, but which itself is different from theactual data input by the user. For example, a user-perceived error inthe monitoring device may be deduced by the user providing externalmeter values with or without prompts, or if the user provides externalmeter values at a greater frequency or number than usual for thatpatient, or at a greater frequency or number than requested by themonitoring device itself

The data 230 can also include information about meals ingested, insulintaken, exercise performed, or the like, and in this case when data 230is entered, an optional step may be to determine data 234 whichrepresents whether the entered data, e.g., a meal response or adistribution of the same, is consistent with prior signal values seenwhen similar meals were ingested or exercise performed. In particular,the comparison may be against prior entered meal/insulin/exerciseinformation 236 known, or the same may be compared with predictivemodels 238. If meal or insulin responses change for a patient for thesame entered meal or carbohydrate count, as compared to previouslydeclared meal or carbohydrate counts of the same amount, then an errormay be suspected and the usability of the data for therapeutic decisionmaking may be in question (e.g., resulting in lower confidence level).The comparison may be with respect to the signal response tocarbohydrates, a rate of change, a duration of the meal response, or thelike. Prompts or queries may also be employed where the same are notnecessarily compared against prior meals, insulin, or exercise, butwhere the prompts attempt to confirm notable readings. For example, ifthe glucose concentration value sharply rises, the query could promptthe user to enter if they just ate a large meal. The responses to suchqueries or prompts can determine if the signal is acting in an expectedor consistent way, or if the signal is acting in erroneous fashionaffecting the usability of the data and potentially causing a modeswitch.

Another type of external or other data 142 includes a time duration 252since the last data push or pull. In other words, if a significant timeperiod has elapsed since the last time data was received by themonitoring device, such may be employed as a criterion in a modetransition. As a specific example, if a long period of time has elapsedsince the device last received data, a pushed or automatic datatransmission mode may be entered, so as to alert the user to theircurrent glycemic state.

Another type of external or other data 142 includes data from anexternal meter 198, e.g., an external blood glucose meter. Such data maybe received on the basis of a prompt, for confirmation or calibrationpurposes, or based on other initiators. Such data 198 is not limited toonly meter reading values, but may also be based on data 212 about anexternal meter type or model. The data 198 may further includecalibration data 204 about an expected range of signal values (given thecurrent measurement) or calibration data 202 related to reliability,e.g., noise values or other signal characteristics values. The externalmeter data 198 may further include data 206 about an uncertainty inmeasured values, including data 208 about a number of data points, theirrange, their rate of change, or the like, as well as data 210 aboutoutlier values.

Another type of external or other data 142 which may be employed,particularly to modify triggers or alter criteria, e.g., thresholds fortransitions, is data 216 about other parallel or concurrent modes inwhich the monitoring device is operating, which may include calibrationmodes, decision-support modes, transmission modes, or any other suchoperating mode. In this implementation it is data about a differentparallel mode that is being employed as an input in the determination ofwhether to switch a subject mode. The other different parallel mode hasalready been presumably determined, e.g., whether by user or deviceselection or by another of the mode switching examples.

One such parallel mode is the decision-support mode 144 (see FIG. 5). Inparticular, any of the “usability” criteria described in FIGS. 6-8 couldbe modified by the system in real time based on a decision support modeof the device. Put another way, the criteria determinative for modeswitching, above, may depend on how the user would like to use the datafrom the device. FIG. 9 is a flowchart 300 illustrating how data aboutother operating modes may be employed as criteria for mode switching ofa subject mode.

In more detail, CGM may have many different uses or indications. Someusers may prefer CGM for general educational and behavioralmodification, e.g., Type II diabetics on oral medications, while othersmay use CGM to dose insulin throughout the day (e.g., Type I orinsulin-dependent Type II patients), or even as a data feed into aclosed-loop artificial pancreas system. The criteria forusability/reliability of data for insulin dosing should be morestringent than the criteria for usability/reliability for educationpurposes. While multiple different CGM sensor designs could be deployedfor each specific indication or use, it would be simpler to provide onesolution for CGM and allow that system to adapt to that solution.Accordingly, in some implementations the monitoring device may requestor require the user to provide information regarding that patient and/orhow the patient intends to use the data.

Accordingly, in the first step of flowchart 300, the monitoring deviceis operating in an original or initial mode 260. The decision ordetermination by the monitoring device to switch to a new mode 262 maystill be based on a factor related to data usability 264 (as determinedby a criterion), but the same may also be based on other data 266, e.g.,about the intended use of the monitoring device (such data is indicatedin FIG. 8 as data 230). Such data 266 may include data 268 about adecision-support type, e.g., data 270 about whether the use isadjunctive or therapeutic, data 272 about a phase in which the device ordevices are operating, the phase representing a level of control, or thelike. It will be understood that data 270 and data 272 may overlap, andthat combinations of such data may be employed.

Data 276 about other uses of the device which could affectdecision-support may also be indicated by the user and included as data266. These may include data 276 about user intentions, or desired useinformation relating to weight loss, exercise impact, post-meal glucosesummary, a desire to select more nutritious foods versus junk food, orthe like. The data 266 may also be drawn from patient current healthdata 278 including data about whether the user is ill, whether the useris experiencing pregnancy or their menstrual cycle, level of activity,type of activity, and the like. The data 266 may further be drawn fromspecific patient goals such as hypoglycemic avoidance, nighttimecontrol, postprandial control, longer duration of the sensor session,and the like.

The data 266 may further include data 274 about user preference forconvenience versus accuracy. In other words, the user can indicate apreference for accuracy over convenience, and the criteria can adjustfor that. For example, the system can call for additional blood glucoseexternal meter calibration values, and such would increase accuracy atthe expense of user convenience. Alternatively, the user can indicatethat they prefer convenience over accuracy, and the criteria can adjustfor that. In this case, the system may perform more deviceself-calibration and less user-dependent calibration. The system can insome cases tighten other controls to attempt to make up for the loss inaccuracy. For example, thresholds for hypoglycemic and hyperglycemicranges may be loosened, so that while accuracy is lessened, a user maybe alerted earlier to the entry into such glycemic states. Userpreference may also be employed to determine which mode to start on.Specific user goals may be employed in this mode determination, e.g., anew user may have more simple or less complex goals, and such can bereflected in the determined mode. Whatever the user goal, the userinterface may be provided with an indicator to clearly indicate thetransition, as well as (in many cases) an indicator of the new mode ofoperation and accompanying new mode of user interaction in which thedevice is operating. The indicator may in some cases expressly indicatethe transition, e.g., textually or verbally, or may indicate the same inother ways, e.g., by the user of color, shapes, pictures, or the like.In some cases the indication is not to a person but to a system, e.g., apump, and in this case the transition will also be clearly indicated by,e.g., a flag or other data indicator. The pump or other downstreamdevice may then treat received data according to the new mode, which mayin some cases be different from how data was treated under the old mode,although such is not necessarily required.

In another implementation, as shown in the flowchart 325 of FIG. 10, thesystem may be employed to detect usage of the device. In a first step, asession begins and/or the monitoring device starts up (step 280). Insome cases the monitoring device starts up in a fully configured mode asenumerated above, e.g., by simply starting up in a last-identified mode.In other implementations the monitoring device powers up and as part ofits initial configuration determines what mode to start in. As part ofthis determination, the monitoring device may detect the usage of thedevice (step 282). For example, the monitoring device may detect that itis signally coupled to a medicament delivery device such as an insulinpump. In this case, the monitoring device may start up in a therapeuticmode, subject to, e.g., optional patient confirmation. The userinterface of the monitoring device may also request input as to theusage of the device (step 284). The system detected usage and theuser—entered information may be employed in a final determination ofwhat mode the monitoring device is set to (step 286). In other words,the system-detected usage and the user-entered usage data may not bythemselves uniquely determine the most appropriate mode, but the two mayact as data inputs and which together (and optionally with other data)determine the device mode. Moreover, in some cases it may not be desiredto have a user enter a particular mode per se, but rather to have theuser enter more user-friendly information that is in turn converted to adetermined mode. For example, it may be more understandable to a user toindicate that their CGM is driving their pump then to necessarily havethe user indicate the more technical distinction that the device is in atherapeutic mode rather than an adjunctive one.

In any case, once the mode is set (step 286), the mode may be switchedaccording to methods above if determined data meets transition criteria(step 288). Specific examples of mode switching are described below.

User-Dependent Calibration Versus Device Self-Calibration

One exemplary type of mode switching is between user-dependentcalibration and device self-calibration, e.g., switching between usingno blood glucose meter readings (e.g., using a priori, internal (e.g.,using impedance), prior derived values, or device self-calibration only)and using blood glucose meter calibration, or vice versa. User-dependentcalibration generally requires ongoing input of external reference data,e.g., glucose concentration meter values, to maintain calibration,although a priori information may be included as a part of thealgorithm.

Additional details of systems and methods for user-dependent calibrationare disclosed in U.S. Pat. No. 7,778,680; and U.S. Pat. No. 7,920,906,which are owned by the assignee of the present application and hereinincorporated by reference in their entireties.

Device self-calibration mode does not require ongoing input of externalreference data from the patient, although previously entered externalreference data may be used to influence the calibration. While it isdesirable to ensure the reliability or stability of deviceself-calibration, one problem that has been encountered is that theusability of device self-calibration may be influenced by a number ofvariables, including: stability of the calibration factors over time invivo; in vitro to in vivo predictability of calibration factors (whichmay be influenced by shelf-life); and patient-to-patient variability ofthe calibration factors, as examples.

Additional details of systems and methods for device self-calibrationare disclosed in U.S. patent application Ser. No. 13/827,119, which isowned by the assignee of the present application and herein incorporatedby reference in its entirety.

Similarly, the usability of user-dependent calibration has challenges aswell, as the same may be influenced by a number of variables, includingthe frequency and reliability of the user's blood glucose calibrationentries. Accordingly, one mode or another may be preferred at a giventime. In certain systems and methods according to present principles,the mode switching between these two modes occurs to preferentiallyselect the mode with the most preferred data usability, with the modeswitching occurring adaptively and dynamically, in real time, based onthe evaluation of transition criteria.

FIG. 11 shows a flowchart 350 for accomplishing calibration modeswitching. Several transition criteria that are particularly useful incalibration mode switching are described in the figure, but it will beunderstood that any of the transition criteria described in FIGS. 5-9may also be employed. Moreover, while FIG. 11 details transitionsbetween calibration modes, it will be understood that other types ofmodes may be running in parallel or concurrently, e.g., decision supportmodes, data transmission modes, and the like.

In a first step, a monitoring device is presumed to be in a prior,original, first, or initial mode (step 290). For purposes of discussionthis mode is shown as a self-calibration mode. In this mode, sensor datais transformed using calibration factors determined by the deviceitself, without recourse to external values.

An optional next step is to determine a usage type (step 292). The usagetype may pertain to a decision-support mode, a user intention, a usergoal, or other aspects as described above. Such data may also pertain toother patient characteristics which require increased safetyrequirements versus convenience, and may thus change the evaluatedtransition criteria. Such patient characteristics may include data aboutgastroparesis, hypoglycemia awareness, etc.

The usage type determined may be assisted by the detection 294 of othercoupled devices, as well as by user input 296. In this regard it isnoted that the safety requirements for a therapeutic mode (e.g., ofwhatever phase) are generally higher than the safety requirements for anadjunctive mode, while the convenience requirements may be higher for anadjunctive mode as compared to a therapeutic mode. Accordingly, thecriteria for switching modes may be weighted more for safety if in atherapeutic mode and may be weighted more for convenience in theadjunctive mode. Accordingly, the optional step 292 of requesting usagetype information is included in the flowchart 350, either as the same isautomatically detected (step 294), e.g., when the sensor is connected toan insulin pump, or prompted as input by the user (step 296). Forexample, one or a series of questions could be asked of the user at thestart of a session or when setting up the device that determines how theuser intends to use the sensor data. For example, a user could select ause case for the data, e.g., therapeutic versus adjunctive, or, e.g.,educational e.g., just watching trends, versus decision-support based.Such data may even be used to determine or modify aspects within a mode.For example, within a user-dependent calibration mode, a monitoringdevice algorithm may adjust to more or less external blood glucose meterprompts, as some users would rather calibrate more often and achievegreater accuracy, e.g., traditionally-treated type I diabetes patients,while other patients would rather not calibrate and do not require ahigh-resolution of accuracy, e.g., type II diabetics on oralmedications. This determination of accuracy can further be used in thestartup mode to drive which mode the monitoring device is initiallyconfigured to, e.g., user-dependent calibration versus deviceself-calibration.

As another example of where the usage type is determined in part bydetection of other devices (step 294), the monitoring device may detectthat it is connected to a medicament delivery pump and may furtherdetect a setting that indicates its use in a closed-loop—decisionsupport configuration for the pump. The monitoring device may thenadjust criteria for calibration modes based thereon, e.g., increasingthe calibration requirements based thereon.

In any case, a next step is to determine the usability of the currentcalibration mode by comparison to one or more criteria (step 298). Inparticular, a data signal, e.g., from the sensor, or other determineddata, is received (step 304), and one or more criteria are received orretrieved (step 302). For example, the criteria may be received frommemory or other local or online storage.

The criteria used may be one or more from the group described withrespect to FIGS. 5-9, but certain exemplary criteria will be describedand/or reiterated here.

One criterion which may be employed includes data that is internallydetected by the monitoring device and which indicates sensitivity. Forexample, as shown in FIG. 6, self-diagnostics which measure impedancecan be employed to detect shifts in sensitivity. Such self-diagnosticsmay be performed on a regular basis or whenever errors are perceived.Where a shift in sensitivity is detected, the same may be employed toinform the device self-calibration or the same may be employed to causea transition to a new mode, e.g., user-dependent calibration, if theshift in sensitivity is greater than a predetermined criterion.

Another criterion which may be employed is based on data from otherconnected devices (see FIG. 8). This “machine-to-machine” checking canbe employed to, e.g., check CGM values against a connected blood glucosemeter. The connection between such devices may be peer-to-peer or over anetwork. In this example, if a blood glucose meter does not match theCGM value, a query may be initiated to either perform diagnosticswithout user input or to prompt the user to perform a step to assist inthe reconciliation of such values.

Referring in addition to FIG. 7, the evaluation of determined dataagainst a criteria may include evaluation of current data againsthistoric values of, e.g., glucose ranges, mean values, patient specificprofiles, patterns, or the like. If the determined data does not followa patient's individual normal glucose profile within certain criteria,e.g., within 25%, 10%, or the like, a patient may be prompted to inputwhether the patient believes the data to be unusually high or low, orwhether the patient perceives an inaccuracy. The patient may further beprompted as to potential reasons for abnormal glucose readings. Thecomparison may be made more granular by comparing based on time of day,day of week, the context of the glucose, e.g., whether the same pertainsto nighttime hypoglycemia, a post-meal high, and so on. The comparisonmay further employ statistical factors such as variance, mean, rate ofchange, the variability of glucose concentration values, or the like.

Other criteria which may be employed include any of those describedabove in connection with FIGS. 5-9, including without limitation, e.g.,user responsive actions to alerts and alarms (e.g., amount of timebetween alert and user acknowledgement), user feedback on alerts (e.g.,levels of perceived accuracy), the entry by the user of outlier data andfalse alarms (e.g., as determined by outlier detection and/or measuresof alarm fatigue), the glucose values resulting from confirmatoryexternal meter readings, which may be prompted for by the monitoringdevice or otherwise provided by the user, meal and insulin data enteredand glucose value responses, as well as comparisons to prior meal andinsulin data, whether sensor trajectory matches that of previouslyinstalled sensors (e.g., profiles associated with break-in, changes insensitivity, etc.), other concurrent operating modes, and so on. Othercriteria may include, if multiple sensors are present, whether one or a“main” sensor reading is consistent with others of the multiple sensors,including situations in which the main sensor tracks the same hormone orbiological analyte as the other sensors as well as situations in whichthe main sensor tracks other hormones or other biological analytes,particularly where such hormones and other biological analytes are knownto bear a relationship with the hormone or other biological analytetracked by the one or main sensor.

In any case, if the usability of the data matches the one or moretransition criteria, a transition may occur to the new mode, e.g., auser-dependent calibration mode (step 310). In this mode, data iscalibrated based on an external meter, e.g., using finger sticks forcalibration (step 316).

In some cases, where a transition to a user-dependent calibration modeis not immediately performed, but where it can be determined that such atransition will likely be necessary, the monitoring device may render adisplay indicating that an external meter reading is likely to benecessary. In this way, a user can be prepared to carry the necessarymeter on their person.

In some implementations, a transition to a new mode may be postponed bythe performance of one or more steps. For example, in the above examplewhere a transition to a user-dependent mode is contemplated, an externalmeter reading may be requested of the user to provide confirmation ofthe device self-calibration (see, e.g., user confirmatory blood glucosedata 228 in FIG. 8). Depending on the results of the external meterreading, the device self-calibration may be confirmed, the deviceself-calibration may be modified or adjusted, or the monitoring devicemay determine that a mode switch is necessary.

Data may then be output accordingly, pursuant to the new mode. Forexample, the monitoring device may show different types of displaysbased on the mode or based on a setting within the mode. In a particularimplementation, for data with high usability, numerical values of bloodglucose measurements may be displayed. For data with lower usability,ranges of data values may be displayed, or the data may appear in aselected color based on the range. Additional details of systems andmethods for displaying data in different formats are disclosed in U.S.Provisional Patent Application Ser. No. 61/978,151, which is owned bythe assignee of the present application and herein incorporated byreference in its entirety. Further details are described below withrespect to FIGS. 18A-18D, which are in the context of displays fordifferent decision-support modes, but which can be extended to differentcalibration modes, data transmission modes, as well as other modes.

Similar consideration pertain to the reverse transition, e.g., fromuser-dependent calibration to device self-calibration. A usage type maybe determined (step 318) as in step 292, particularly if the usage typehas changed since the occurrence of step 292. Usability of data isdetermined by comparing determined data against one or more criteria(step 320), and if the usability of the data matches one or morecriteria, a transition may be caused (step 322), e.g., back to thedevice self-calibration mode (or to another mode).

In general, if calibration parameters evaluated meet certain criteriaindicative of necessary external reference data for calibration, theuser-dependent calibration mode may be entered. On the other hand, ifcalibration parameters meet other criteria indicative of sufficiency ofdevice self-calibration going forward, the device self-calibration modemay be entered. In some cases a user may be prompted before switching todevice self-calibration, and the prompt could be informational orrequire confirmation by the user prior to the mode switching.

A more detailed flowchart 375 is depicted in FIG. 12 for this aspect. Inaddition, specific switching criteria from user-dependent calibration todevice self-calibration are also shown and described.

In this particular scenario of the reverse transition from an initialuser-dependent calibration mode 324 to a new device self-calibrationmode 326, one criterion which may be employed is whether or not the userhas entered external meter values, or a sufficient number thereof. Ifthe user has not, then the monitoring device may transition to aself-calibration mode until an external meter value, or a sufficientnumber of the same, are entered, which may then trigger user-dependentcalibration.

Other criteria which may be employed include any described above inconnection with FIGS. 5-9, as well as other variables and parameters,particularly those related to a determination (step 328) of theusability of the device self-calibration over time, e.g., error atcalibration, an individual user profile, and the like. One way todetermine the usability of the device self-calibration is by theevaluation (step 330) of device self-calibration parameters, which aresubject to ongoing calibration input (step 332). A general goal incertain implementations is to ensure the reliability, accuracy, and/orstability of the device self-calibration for an individual patient,especially the first time a patient uses the device, so that the samemay be safely transitioned into from a user-dependent calibration mode.Once sensor usability properties have been determined, e.g., stability,accuracy, reliability, confidence, or the like, the same may be seededinto internal calibration parameters for the remainder of the sessionand even in some cases for future sessions.

Accordingly, one transition criteria which may be employed includes dataabout whether calibration parameters from external meter readings arewithin a particular confidence interval or otherwise reliable or stable(step 334), e.g., no more than a certain percentage change, e.g., 5, 4,3, 2, or 1% over one day, two days, or the like, and whether the samematch those determined by the device self-calibration. A relatedtransition criteria include whether calibration parameters are within anexpected range (step 336) based on a priori or predetermined internalmeasures, e.g., within the same range as a set of previouspatient-specific calibration parameters, within expected factorydetermined ranges, and/or within expected internal testing ranges, e.g.,as determined by impedance measurements. Other transition criteriainclude confidence in the user's external blood glucose meter used forcalibration, as well as the user ability to enter a correct reading fromthe meter. Such can be determined by analyzing meter data (step 338)based on analysis of a number of outliers detected during a calibrationroutine (step 340), analysis of data about the meter brand and type(step 342), or the like. In this example, the criteria for modeswitching may be at least in part dependent upon a number or type ofoutliers detected (e.g., number of BG reference values that appear inaccurate based on outlier detection criteria). Yet additional relatedmeter related data include the uncertainty of the blood glucose metervalues (step 344) entered for calibration based on analysis of a numberof factors (step 346) including the number of values available, e.g., asmore values means a more accurate regression; glucose range, as moredata points spread across a full glucose range again allows a moreaccurate regression and subsequent linearity across the range; and/orrate of change, because time lag between interstitial glucose andcapillary glucose are more noticeable when glucose is rising or falling.In these examples, the criteria for mode switching may be at least inpart dependent upon a certainty level threshold of such data.

In some cases, the determination of whether glucose values andcalibration parameters are within expected ranges may be determinedprobabilistically, and details about such methods are provided in WO2014/158327A2, owned by the assignee of the present application andherein incorporated by reference in its entirety.

Yet another related transition criteria may relate to the calibration orconfirmation external meter reading value itself, particularly where thevalue is the same as a default reading. For example, in someimplementations, a monitoring device may default to 120 mg/dL for manualentry of blood glucose concentration values. The monitoring device maybe configured to question or to give a lower confidence in defaultvalues that are regularly accepted, particularly where such are found tobe outliers.

Other transition criteria may include decision-support mode, e.g.,whether the use is adjunctive or therapeutic, or the like. Yet othertransition criteria may be as described above, including those employinguser input (step 348).

In a particular example of mode switching between user-dependentcalibration and device self-calibration, a user may start withuser-dependent calibration for a first sensor in a four pack of sensors,where the four pack of sensors are all from the same manufacturing lot.Once calibration is established for the first sensor usinguser-dependent calibration, the next three sensors in the pack/kit couldswitch to device self-calibration based on the calibration of the firstsensor in the pack. This ability is predicated on the likelihood thatdifferent sensors within the same lot have similar or the same specificcharacteristics that when used by the same host, will perform in asimilar manner (similar correlations could be made for different hostsusing the sensors from the same lot). As noted, such lot data may bedetermined using analysis of metadata.

As noted above, while mode switching may occur to user-dependentcalibration or to device self-calibration, generally the latter ispreferred if possible due to increased user convenience. FIG. 13illustrates a logical diagram and accompanying chart which show howpresent principles may be employed to gain confidence in deviceself-calibration. In the solution of FIG. 13, both user-dependentcalibration (mode 354) and device self-calibration (mode 356) are run inparallel. Upon receipt of inputs or information, when confidence in thedevice self-calibration satisfies certain criteria, e.g., exceeds apredetermined threshold, then no additional external meter readings maybe required for calibration. In this case, the output is entirely basedon the device self-calibration. In other implementations, the outputs ofboth algorithms may be combined and weighted accordingly to arrive at adisplayed output.

In such a parallel system, the output of each calibration mode algorithmis evaluated against criteria 360, and the accuracy, reliability,stability, and/or confidence in the different calibration parametersresulting from each of the two modes running in parallel is compared, oralternatively, the usability of the signal from each of the twocalibration mode algorithms is compared, particularly over an optionaltransition region 358.

For example, one or both outputs may be compared to incoming bloodglucose concentration values. To ensure accuracy over a range, thecompared blood glucose (external meter) values may be provided andcompared at extreme high and low values 352.

Other determined signals and transition criteria include any of thetransition criteria described above, but certain types will be describedbelow as particularly useful in certain implementations. Such mayinclude internal inputs, such as the effectiveness of self-diagnosticsor internal calibration information, e.g., impedance. Yet other signalanalysis criteria include comparisons of slope and baseline fromuser-dependent calibrations versus slope and baseline from deviceself-calibration algorithms. Such may be particularly employed toexamine correlations to a prior session. Such exemplary implementationsare particularly useful in that some patients have certain physiologicaldifferences, such as a typically high baseline, as compared to patientswith, e.g., a low baseline, and such comparisons may thus be useful incases where baseline estimates are made.

Yet other sorts of signals and transition criteria include those relatedto fault discrimination, which may be employed to determine a type ofartifact or the like. During such times of artifact presence, theuser-dependent calibration may not be relied upon, or its influence maybe lowered in the weighting. For example, if compression artifacts arediscriminated, and a blood glucose meter value is entered into thesystem at about the same time, and the device self-calibration modeshould be relied upon more heavily.

Yet another sort of signal and transition criteria relates to values andestimated errors in values of calibration parameters, includingcalibrated slopes, baselines, and/or drift values for each algorithmindividually (as opposed to the values themselves noted above). In thisimplementation, wherein the device self-calibrated values anduser-dependent calibrated values are roughly equivalent statistically,e.g., such agree within +/−10%, then it may be considered safe to switchmodes, e.g., to drop user-dependent calibration and only operate thedevice in a self-calibration mode. An example of such equivalency isshown in FIG. 13B, in which axis 365 represents sensor readings and axis367 represents glucose values. Error bars 369 a, 369 b, 369 c, andgenerally 369 i represent a range of readings for different glucosevalues for which, if a reading falls within the error bars, the deviceself-calibration may be said to be valid. The points shown representmatched data pairs from external measurements, e.g., for auser-dependent calibration, these points falling along a line 371. Asthe user-dependent calibration values are within the error bars of thedevice self-calibration, the accuracy of each may be consideredequivalent by the system, and consequently the user-dependentcalibration may be dropped in favor of only device self-calibration forfuture calibrations during the session (until and unless a subsequentdetermination of device or data usability indicates that user-dependentcalibration should once again be initiated).

Additional details of such aspects may be found in the WO 2014/158327A2publication incorporated by reference above.

As noted above, the signal and transition criteria may be dependent onother modes, which may dictate that one or another mode is switched to,or may also indicate that the two modes may and should continue to runin parallel.

A weighting may be applied to one or both of the values from therespective mode algorithms, and weighting of one output may be greaterthan that of the other due to confidence in data from the respectivemode. For example, if parameters in the device self-calibration suggeststhe same has an 80% confidence level, the output from the deviceself-calibration mode may be assigned an 80% weighting, leading to thevalue from the user-dependent calibration being assigned a 20%weighting. This type of weighting scheme 362 could occur iteratively anddynamically over time until one of the algorithms outweighs the other byso much, e.g., 95% to 5%, that the weighting itself is a criterion thatallows for the triggering of the new mode. This is seen in FIG. 13Awhere a user-dependent calibration mode I is indicated on the left sideof the diagram, a device self-calibration mode II is indicated on theright side of the diagram, and a transition region III is indicatedwhich is an average or a weighted average of the outputs of the twoalgorithms. The portion of the curve 359 indicates a weighted average,while the portion of the curve 361 indicates a section in which theweighting has become heavily towards the user-dependent calibrationmode, and upon meeting certain criteria, e.g., passing a thresholdtransition level 364, the monitoring device is caused to completelyenter the user-dependent calibration mode. Similarly, the portion of thecurve 363 indicates a section in which the weighting has become heavilytowards the device self-calibration mode, and upon passing a thresholdtransition level 366, the monitoring device is caused to completelyenter the device self-calibration mode.

Whether a single mode or a combination of modes is employed, calibrationmay be performed and sensor data may be output and displayed based onthe calibration mode determined.

In a variation that combine concepts according to present principles, ahybrid mode may be entered in which the device operates under deviceself-calibration until such time as a user indicates via an appropriateentry on a user interface that the user believes the shown value,according to device self-calibration, is incorrect or off. In thishybrid mode, the user may enter a fingerstick SMBG value as a check onthe operation of the device under device self-calibration. The valuemeasured and entered in the CGM may be used as part of the calibrationof the device, and the weighting of the value may decrease as timepasses. Such a value may be used to adjust the factory calibration. Insome implementations, no additional SMBG values need be entered. Inother implementations, subsequent SMBG values may be entered, e.g.,every 12 hours, every 24 hours, and so on. Such calibrations may serveas an iterative update calibration, increasing the accuracy of the CGMreading.

Scheduled and Unschedule Modes of Data Transmission and Display

Referring to the flowchart 425 of FIG. 14, another exemplary type ofmode switch involves switching between different data transmissionmodes. In one implementation according to present principles, one datatransmission mode is termed “unscheduled” and another is termed“scheduled”. Scheduled transmission of a sensor transmitter to areceiver or smart phone (or, e.g., a wearable device such as a watch)generally correspond to regular periodic transmissions of data fordisplay and analysis, though the same encompass non-periodictransmissions as well. Unscheduled transmissions may be broken down intoevent-driven unscheduled transmissions and user-driven unscheduledtransmissions. Event-driven unscheduled transmissions may occur when thedevice notices a trend for which the user should be made aware, e.g., animpending hypoglycemic or hyperglycemic event. User-driven unscheduledtransmissions may correspond to “on demand” types of transmissions,e.g., where the user pulls data from the sensor and transmitter (orreceiver/smart phone) because they wish to view their current status. Insome cases pulled data is desirable because it minimizes battery drain.However, such modes do not give the user the most information, unlesseven driven unscheduled transmissions are also enabled.

While automatic transmission of periodic data may provide more timelydata to a user without required action by the user, it can significantlydrain the battery power of on-skin sensor electronics connected to theon-skin device (as well as the receiver), which may create difficulty inminiaturizing the electronics sufficiently for comfortable wear andinexpensive manufacture. On the other hand, on-demand transmissionglucose sensor data may also be employed, e.g., responsive to userrequests, e.g., using near field communication. Unfortunately, on-demandtransmission does not allow for timely alerts and/or alarms to bepresented to the user if they do not request data at the appropriatetime.

One solution is disclosed below is to provide dynamic switching betweenon-demand and automatic transmission modes of operation. In this way,both of the requirements noted above can be satisfied. In particular,switching occurs between a data transmission mode that stores or buffersdata for on-demand transmission triggered by user request and a mode ofautomatic transmission of data triggered by a condition, e.g., a largeexcursion, the meeting of alert criteria, a glucose concentration valuewithin a glycemic danger zone, or data delivered at a predetermined oruser set frequency, e.g., once every 5 minutes, once every 15 minutes,once every half-hour, once every hour, or the like.

The mode switching may occur either at the monitoring device or at thesensor electronics. In the first case, the monitoring device can send asignal to the sensor electronics to indicate, prompt, or cause the modeswitch. The data generally requires presence at the monitoring devicefor such a determination to be made. However, the determination toswitch modes may also occur at the sensor electronics, i.e., where thesignal is initially received from the in vivo sensor.

In a first step of the method, the monitoring device starts in a firstor initial mode of data transmission, which may be the mode the deviceinitializes in, a mode the device has switched to in a prior step, orthe like. In FIG. 14, an exemplary initial mode of on-demand datatransmission is shown (step 368). Data is transmitted according to thismode (step 372) from the sensor electronics operably connected to theon-skin sensor device, and the same is rendered on the monitoring devicedisplay. In on-demand data transmission, sensor data is generatedresponsive to a request by the monitoring device, e.g., a user-promptedrequest.

In a next step, data is evaluated against one or more criteria (step376), and if the criteria, or, e.g., threshold associated with the same,is met, a mode switch occurs (step 370), in this example to a periodicor automatic mode of user interaction. Data may then be transmittedaccording to the new mode (step 374).

The reverse mode transition or switch can also occur, where in theperiodic or automatic transmission mode, data is evaluated against oneor more criteria (step 386), and if the requisite criteria is met ormatched, a transition may be made to the on-demand mode of userinteraction. Hybrid modes are also envisioned.

The criteria applied may be any of those noted above with respect toFIGS. 5-9, and certain exemplary such criteria are noted below.

One transition criteria of particular use in data transmission modeswitching includes analysis of large excursions (step 378), which may bepoint-to-point changes in glucose concentration values above a certainchange threshold, or similarly large excursions in rate of change orglucose acceleration as compared to thresholds. Predictions about suchvalues may also be used in these calculations. Determining that aglucose concentration value is in a glycemic danger zone (step 382) mayalso be employed, where upon a user's glucose concentration valueentering such a danger zone (where the edge of the danger zonerepresents a criterion such as a threshold), a mode switch may be causedto happen. Similar criteria may be defined for GUI thresholds or otherglucose-related values. Notably, the device initiated transmission maybe to a dedicated receiver/phone and/or to the “Internet of Things”depending on user preferences.

Another transition criteria of particular use in data transmission modeswitching includes the amount of time since a user last requested avalue. For example, a determined criterion may be the amount of timesince a last user request for data, and a transition criteria may be athreshold duration such as eight hours. If the determined data isgreater than the threshold criterion, e.g., if it has been more thaneight hours since the last user request for data, a mode switch mayoccur, e.g., to an automatic data transmission mode.

Another transition criteria which may be applicable to transmission modeswitching involves an analysis of alert criteria (step 380), e.g.,user's responses to alerts or prompts provided on the user interface ofthe monitoring device. For example, where a mode switch may bepotentially indicated, a user could be asked to confirm data on whichthe mode switch was predicated. A lack of such confirmation, or acontrary indication, may lead to the potential mode switch beingsuppressed or otherwise not performed.

Other transition criteria 384, as noted herein, may also be employed. Inparticular, data about other concurrent or parallel modes may beemployed to inform data transmission mode switching. For example,whether the user is in a therapeutic mode versus an adjunctive mode, orwhether a monitoring device is calibrated on the basis of deviceself-calibration versus user-dependent calibration. The decision-supportmode may be particularly useful criteria to evaluate for mode switchingin data transmission. For example, the system may detect that it isbeing relied upon for a portion of a closed-loop algorithm. In such amode, automatic transmission may be a default setting, so that themonitoring device receives information needed to drive medicamentdelivery pumps. On the other hand, in an adjunctive mode, differenttransmission criteria may apply. For example, where the monitoringdevice is only being employed for educational purposes, an on-demandtransmission mode may be sufficient. Transmission criteria may also bebased on phase or mode where such are provided as different phases ofcontrol of an artificial pancreas system as illustrated in FIG. 15A.

Yet another transition criteria which may be employed, particularly in atransition or mode switch from an automatic data transmission mode to anon-demand mode, relates to analysis of data (step 388) about remainingbattery power. For example, if sensor electronics are running low onbattery power, so long as the system can determine the user is protectedand otherwise within a safe glycemic state (and appears likely to remainso), an on-demand mode of user interaction may be entered in order toconserve remaining battery power.

Specific examples are now provided.

In one example, the device may start in an on-demand mode, but if theuser's glucose is in a dangerous zone for a predetermined period oftime, then the device may switch to an automatic transmission mode. Sucha mode may be maintained until, e.g., a user acknowledges an alert oractively requests to switch back to on-demand mode. In another example,the device may start in an automatic mode, but if the user's glucosestays within a desired range for at least, e.g., four hours, then thedevice may switch to on-demand mode. The on-demand mode may bemaintained until the user's glucose concentration value travels out ofrange, is predicted to go out of range, or otherwise enters anundesirable state.

In another example, mode switching may occur based on the use to whichthe user desires to put the data. In this regard it is noted thatcertain analyte monitors employ ‘beacon’ signals which emanate from thetransmitter and which a receiver device, e.g., a dedicated device, smartphone, or the like, may then engage. Such beacon signals are disclosedin, e.g., US PGP 2013/0078912 and US PGP 2015/0123810, owned by theassignee of the present application and herein incorporated by referencein their entireties.

For simplicity the case of a smart phone will be discussed, but thesituation for a dedicated receiver is similar. The smart phone maychoose to engage the transmitter upon receipt or detection of a beaconsignal, and following a handshaking step data may be transferred. Insome cases, a mode may be entered in which communications do not occurwith every beacon signal. For example, in the case of a user employingthe CGM monitoring for purposes of health or fitness optimization, orweight loss, it may not be desired to receive data with every beaconsignal. A benefit to not receiving transferred data with every beaconsignal includes battery life, but other benefits will also beunderstood.

To enable the above, the system may be configured such that the smartphone does not even poll for beacon signals for a certain period oftime, e.g., over the period of several beacon signals. Alternatively,the transmitter may enter a mode where it does not emit a beacon signalfor a period of time. For example, instead of emitting a beacon signalevery 5 minutes, it may emit one only every 15, 30, or 60 minutes. Suchdata will generally have less resolution and less actionable, butdepending on the use to which the data is put, such resolution may besufficient.

In another example, in an on-demand mode, if the user desired to basetherapy on the data, and if the user was employing a communicationsscheme such as NFC, the user may perform a device ‘swipe’ which would beitself begin to enable the user to receive higher resolution data.First, the swipe would result in an initial higher resolution of data.Second, the swipe may in some implementations be used to indicate to thesystem that higher resolution data should be obtained.

Decision-Support Modes, e.g., Therapuetic (Non-Adujunctive) VersusAdunctive (Non-Therapetic) Mode Switching and Mode Switching BetweenPhases Therein

Yet another exemplary type of mode switching involves switching betweendifferent decision-support modes, e.g., therapeutic, adjunctive and/orphases of control of an artificial pancreas system as described withreference to FIG. 15A. In more detail, currently all commercial CGMdevices in the US are used adjunctively, which means that the devicedoes not replace the information obtained from a standard home bloodglucose meter but rather is used to complement the information obtainedfrom the blood glucose meter. Patients are instructed to make therapydecisions, e.g., insulin dosing, based on the meter value, rather thanthe CGM value. For patients, it would be preferable for a device to beused therapeutically, which means the information obtained from the CGMcan be used to determine therapy decisions, e.g., insulin dosing, forthe patient with diabetes.

One issue with providing varying levels of control based on CGM usage isthat the performance of the device, and the use of the information fromthe device, may differ from patient to patient. Even in a devicespecifically intended for therapeutic use, it would be advantageous todetect and trigger an adjunctive mode if the usability of the data for aparticular patient does not meet certain standards, e.g., where theusability relates to factors noted above, e.g., accuracy, stability,reliability, or confidence in the data. A solution is to evaluate theusability of the sensor data and to switch between therapeutic andadjunctive modes in real time accordingly. As described in greaterdetail below, the system may be further extended to include levels oftherapeutic control, e.g., as described by phases in an artificialpancreas system as described in connection with FIG. 15A.

More generally, the therapeutic/adjunctive paradigm may be broadened toa spectrum of levels of control of pump function, from an adjunctivemode where the CGM does not control the pump in any way, up to a pointwhere analyte monitors control all medicament delivery, e.g., insulin aswell as others, and no patient involvement is needed. In one exemplaryimplementation, the spectrum of levels of pump control may be the phasesshown in diagram 445 of FIG. 15A. In this diagram, the phases progressfrom the system exerting no control to the system exerting the mostcontrol.

FIG. 15B illustrates schematically an exemplary such artificial pancreassystem 411. In this system, control is exerted to various extents inrespective various phases (in the implementation of FIG. 15A, for phasesabove and including phase 1). Generally, a control system 421 receives asignal from an analyte sensor 415 in a biological system 413 and exertscontrol over the analyte concentration in the biological system bycontrolling intake of one or more substances into the biological systemusing, e.g., a medicament administration device 417. The medicamentadministration device 417 may include, e.g., a pump, an IV, and/or oneor more other devices which can controllably administer a substance intoa body. In an artificial pancreas system, the analyte sensor may be thatassociated with a continuous glucose monitor, and the one or moresubstances may include insulin administered by pumps or injections. In amore advanced system, other analytes may be monitored including insulin,and the one or more substances may include, e.g., glucagon. Extendingbeyond the artificial pancreas, control may be extended in this way toother hormones besides insulin. While the control system 421 isillustrated in FIG. 15B as being situated within a display system 419,the same may also be situated as part of the sensor and moreparticularly as part of sensor electronics (see control system 421′within analyte sensor block 415) or as part of the medicamentadministration device 417 (see control system 421″). Alternatively,control system modules within two or more of these blocks may worktogether to accomplish both the control required of the phase and thepotential mode switching according to implementations described here.

The use of an artificial pancreas system according to FIGS. 15A and 15Bin the context of the described mode switching implementations caninclude the use of the mode switching implementations to drive, base, orinform switching between the various described phases of FIG. 15A (orother phases which may be developed). The use can also include use ofthe operating mode or phase to drive, base, or inform display on a userinterface of the display system 419. In particular, the display on theuser interface may include an indication of the monitored analyte, aswell as in some implementations an indication of the mode or phase inwhich the system is operating, i.e., the mode of user interaction. Forexample, the user interface may display the glucose concentration aswell as an indication that the artificial pancreas system is controllingfor hypoglycemia but not hyperglycemia. In another example, the userinterface may provide an indication that the user is, e.g., in “Phase 6”and that all control is currently being provided by the system. Numerousvariations will be understood given this teaching and the examplesprovided below.

The display system 419 is described in greater detail below with respectto FIG. 18 and FIG. 24, but here it is noted the same may constitute adedicated receiver or a general purpose device such as a mobile device,e.g., a smart phone.

Referring back to FIG. 15A, in an initial phase, i.e., Phase 0, shown byreference numeral 449, the use may be adjunctive only. In other words,the CGM is not used for any sort of pump control. Significant levels ofinformation may still be provided in this phase, as well as in the otherphases. However, such information may be indicated on a user interfaceso as to be only for tracking purposes, determining trends, or foreducational purposes. For a user who is pre-diabetic, phase 0 may be theonly phase needed.

In a next phase, i.e., phase 1, shown by reference numeral 451, aninsulin pump may be controlled so as to turn off at times when the useris encountering low glucose levels. In phase 1 and subsequent phases, itwill be understood that while pump actions are disclosed, the same maybe accomplished by an indication on a user interface of the monitoringdevice directly employable by a user for dosing, e.g., by injection,ingestion, or the like.

In a subsequent phase, i.e., phase 2, shown by reference numeral 453,phase 1 may be enhanced, e.g., by allowing hypoglycemia predictions tooccur, and causing alarms when such conditions are present or likely tooccur. If such alarms go unheeded, a phase 2 system may cause areduction or cessation of insulin if the user's glucose level is below athreshold.

In a next phase, i.e., phase 3, shown by reference numeral 455, phase 2may be enhanced, e.g., by including a step of insulin dosing when theuser's glucose level passes above another threshold, i.e., a highthreshold. In a subsequent phase, i.e., phase 4, shown by referencenumeral 457, the system may be essentially closed loop, except formealtime manual assist bolusing. In particular, the system may causeinsulin reduction or cessation at low glucose levels, and insulin dosingat high glucose levels. However, due to glucose variability at mealtimes, bolusing at such times may be performed manually.

In a next phase, i.e., phase 5, shown by reference numeral 459, suchmealtime manual assist bolusing may be removed, and the system may beclosed loop for insulin. In a final phase, i.e., phase 6, shown byreference numeral 461, the closed loop system may be extended from justinsulin to contemplate and control other hormones as well, for a closedloop multi-hormone system.

In one implementation, therefore, the above mode phases may be employedin a control scheme, and mode switching may occur between the variousphases. While the modes or phases or have been described in the order ofleast control to most control, there is no requirement that the systemproceed in such an order (in either direction) in the control of atherapy. Each mode or phase is stand-alone, and may be entered or exitedindependently according to the determined data and mode switchingcriteria. For example, the system may transition from phase six to phasezero, and more generally from phase i to phase j, where i and j are anyof the phases zero to six.

Mode switching between phases is illustrated in greater detail by theflowchart 450 of FIG. 15C. An exemplary initial mode or phase is posited(step 392). Data is transmitted according to this mode (step 394), andsuch may indicate (step 428) the use of the data. If the phase has atherapeutic component, the display may so indicate. For example, the UImay indicate that the data determined may be used to treat diabeteswithout confirmatory external meter values for, e.g., insulin dosing. Inso doing, the data may be employed by the user to determine insulindosing in the context of a bolus calculator or the data may be directlytransmitted to an integrated pump controller. In the language ofartificial pancreas phases, the UI may indicate the current phase, e.g.,“Phase 1—System Will Stop Insulin at Low Glucose”.

In a next step, data is evaluated against one or more criteria (step402), and if the criteria, or a threshold associated with the same, ismet, a mode switch occurs (step 396), in this example to a second mode,e.g., a different phase. For example, the mode may switch to Phase 0,and the UI may so indicate by a displayed notation such as “Phase0—Readings are for Adjunctive Use Only”. Data may then be transmittedaccording to the new mode (step 398). Such may include displaying thedata to a user in such a way to indicate its usability according to thissecond mode or phase (step 412), e.g., in the above example where Phase0 was switched to, the display may indicate that other information suchas external meter values should be used to make actual treatmentdecisions for the user's diabetes. In this specific example, the datadisplayed may also provide an indication of user interaction, e.g., thatCGM data may not be relied on fully for calculating insulin dosing,whether used in a bolus calculator or in an integrated pump controller.

The reverse mode transition or switch can also occur, where in thesecond mode or phase, data is evaluated against one or more criteria(step 414), and if the requisite criteria is met or matched, atransition may be made back to the first mode or phase or to a thirdmode or phase.

The criteria applied may be any of those noted above with respect toFIGS. 5-9 (step 410), including data usability, and certain suchcriteria are described in greater detail below.

For example, therapeutic use for a pre-diabetic user or patient may onlyrequire relative confidence that the display is providing an accuratezone of glycemia and/or an accurate post-meal rate of change. So long asthese are believed to be accurate to a predetermined criterion, e.g., athreshold level, then the device may be essentially therapeutic innature. In contrast, for an intensively-managed insulin-dependent type I(or II) diabetic, the therapeutic mode may have criteria that requires acertain level of accuracy or confidence in the full range of glycemia(40-400 mg/dl) and at all physiological rates of change.

As a particular example, in a therapeutic mode, a user may be instructedto examine the display device, e.g., receiver or smart phone, and verifythat at least three acceptable data points in a row have been receivedimmediately prior to the time period in which the therapeutic decisionis to be made, e.g., dosing. Instead of user examination, the system mayautomatically only make a decision recommendation if the threeimmediately prior consecutive values exist, have been reported, or are“good” or “satisfactory” according to a predetermined criteria. Forexample, the previous values may be prior “five-minute” values, wherevalues are transmitted from the sensor and received in the receiverevery 5 minutes. Additionally, based on prior sensor data values, atrend of the glucose value may be determined and displayed, e.g., atrend arrow may be displayed indicative of whether the glucose value isrising or falling (and may also indicate a rate of change). Such aspectsare important in the determination of a therapeutic recommendation, andthus by requiring the same an additional level of safety is achieved. Inone specific implementation, three immediately prior trend points arerequired, along with a trend arrow and a sensor glucose reading, beforetherapeutic decision support is allowed based on the CGM data. In somecases, in the absence of such, the adjunctive or non-therapeutic modemay be entered. In other cases, again in the absence of such data, thetherapeutic mode may be maintained, but with an appropriate warning tothe user, or alternatively an indication as to how clinically actionablethe data is, e.g., whether dosing may be based on the same or not.

While three immediately prior consecutive values have been disclosedabove, it will be understood that the number of values, their timing,and whether they are consecutive may vary, and in general the priorvalues may be such that a trend can be determined.

The trend or data points discussed above are generally displayed on agraph, and user examination can reveal if the immediately prior threedata points appear and are consistent with other trend data or userexpectations. Such examination and determination are easily performed ona receiver or smart phone or other such device with a similar formfactor. However, if the device is smaller, such as the user interface ona smart watch, screen real estate may be insufficient for display of atrend graph. In this case, trend arrows may be displayed, along with thecurrent glucose value, and the same employed by the user for therapeuticdecision support. The slope of the arrow may indicate the velocity ofthe analyte concentration value trajectory, or the same may be indicatedwith multiple arrows, e.g., one up arrow for a slow rise, two up arrowsfor a moderate rise, and three arrows for a rapid rise (withcorresponding down arrows for decreasing analyte concentration values).In another variation, the user interface of the wearable device, e.g.,smart watch, may directly indicate the clinical or therapeuticactionable nature of the data. For example, the user interface itselfmay indicate whether it has received immediately prior data values,particularly of sufficient quantity to indicate a trend, e.g., bycomparing the number of values it has received with the number of valuesit should have received given the known transmission rate of the datafrom the sensor.

In light of the above, one transition criteria of particular use in modeswitching between therapeutic and adjunctive modes, or between phases ofcontrol as in FIG. 15A, includes a step of determining decision-supportuse (step 403), which may in turn control the criteria for determiningusability of data (step 402).

Another type of data and accompanying transition criteria which may beadvantageously employed in certain implementations of such modeswitching include those related to detected faults, e.g., the likelihoodof an “end-of-life” or other failure mode. Such faults may be detectedby comparing current data to known failure modes as identified bysignatures or patterns in trace data. Additional aspects are describedelsewhere and in US Provisional Patent Application Ser. No. 62/009,065,incorporated by reference above.

Another aspect which may bear on criteria employed includes use (step424) of preset default modes. That is, modes may default to predefinedones based on factors such as, e.g., time of day, glucose level, or thelike. As one example, a user may desire that their device be usedadjunctively during the day and therapeutically at night, e.g., withvarying levels of control and more granularly as the phases shown inFIG. 15A, e.g., to warn and control against low glucose at night (phase2), but to give the user more control during the day (only phase 1).

Yet another type of data and accompanying transition criteria includethose relating to glucose context, e.g., whether the glucose context isnighttime hypoglycemia, adjusting insulin dosing, or other suchcontexts. For example, for the same patient, a therapeutic mode, e.g.,phase 1 control, may be used during nighttime hypoglycemia whereas anadjunctive mode may be used when determining insulin dosing so as totreat hyperglycemia.

The evaluation against transition criteria could further employinternally detected parameters to detect shifts in sensitivity includingthose from self-diagnostic routines, e.g., using impedance to detectshifts in sensitivity as discussed above and further in the applicationsincorporated by reference above. The same may be performed on a regularbasis or when an error is perceived.

Another type of data and accompanying transition criteria which may beemployed involves the use of user-perceived error (step 408). Asdescribed above in the context of other types of mode switching, if auser specifically indicates an error or provides a greater than averagenumber of external meter values, or provides external meter valueswithout prompting, a user-perceived error may be inferred. Accordinglydata usability may be decreased and/or the user may be prompted to enterone or more reasons as to the perceived errors.

The evaluation may include a comparison of current or recent data tohistoric glucose concentration values, e.g., ranges, means, patientspecific profiles, or the like. If the data does not follow thepatient's individual normal glucose profile according to the criteria,e.g., within a threshold, the patient may be prompted as to whether theybelieve the data to be unusually high or low, whether the patientotherwise perceives an inaccuracy, e.g., feels that an alert or alarmconstitutes a false alarm, or whether the patient has a reasonexplaining an unusually high or low or abnormal reading. Similarly, anoption may be provided on the user interface for the user to flagoutlier data.

In the same way, the user interface could provide an option for thepatient to state whether they feel they have high blood sugar or lowblood sugar, and the monitoring device may perform a system check todetermine whether the determined glucose concentration value isconsistent with the feeling. Such an option may also be employed toidentify hypoglycemia unawareness in a patient, e.g., by asking whetherthe patient feels low when CGM data shows that the patient has entered ahypoglycemic range.

In a similar way, the monitoring device could identify an event, e.g.,such as high or low glucose, and ask the user to identify feelingsassociated with the current event, or potential causes for the event. Inboth cases, the user may select from a list of potential feelings orcauses.

In some cases, the monitoring device could prompt for a confirmatoryblood glucose meter reading. Such a meter reading would not necessarilybe used for calibration, but as confirmation of a reading from the CGM.The value may be used to adjust calibration parameters or just forinformation.

The evaluation may depend on other parallel or concurrently runningmodes (step 412). In particular, transition criteria or triggers may bemodified depending on such other modes. In one case, a data transmissionmode may be used in the determination of decision-support modeswitching. In another case, a calibration mode may be used in the samedetermination.

User-selected responsiveness may also be employed as described above. Inparticular, users may select whether they prefer smooth data with a timelag or noisy data that is more responsive to actual glucose changes.

Whatever the criterion or criteria used, if determined data satisfiesthe criteria or criterion, e.g., meets, matches, or exceeds a thresholdlevel, or in another way satisfies the criteria or criterion, modeswitching may occur. In some cases, the subject mode switching, e.g.,therapeutic versus adjunctive, or from one phase to another, may beaccompanied by other mode switching, e.g., device self-calibrationversus user-dependent calibration.

Whether to switch modes, or what mode to switch to, may also be based ona glucose range the patient is currently in. Put another way,range-dependent triggers may be employed (step 406) to inform modeswitching. For example, a first mode may be triggered only forhypoglycemia, while a second mode is triggered if the patient ishyperglycemic.

FIG. 16A is a flowchart 700 illustrating a particular implementation ofmode switching between different phases of an artificial pancreas systemas described in connection with FIG. 15A. In this figure, an initialphase (I) 712 is illustrated which upon occurrence of a trigger 716transitions the operating mode to phase (J) 714, generally with a newmode of operation and a new mode of user interaction, particularly asdisplayed on a user interface of the monitoring device.

FIG. 16B illustrates a particular set of transitions between phases. Forexample, a system in phase 6 (802) may be caused to transition to phase5 (804) upon the occurrence of a particular hormone's sensor failing. Asystem in phase 5 (804) may be caused to transition to phase 4 (806)upon the occurrence of an event such as user detection of perceivederrors, especially ones that relate to the accuracy of mealtime highs. Asystem in phase 4 (806) may be caused to transition to phase 0 (808)upon the occurrence of a lowering of signal usability, because thelowered signal usability may cause an accurate prediction to no longerbe possible. As another example, a system in phase 0 (808) may be causedto transition to phase 1 (810) upon the obtaining of a signal of greaterdata usability, allowing the system to obtain accurate readings again.

Numerous other types of transitions will similarly be understood giventhese teachings, and a non-exhaustive set of such transitions are shownbelow in Table I.

TABLE I TRIGGER(S) (exemplary and ORIGINAL MODE non-exhaustive) NEW MODEFirst level of user Change in usability of data Second level of userinteraction interaction User-dependent Increasing usability of self-Device self-calibration calibration calibration (factory) Deviceself-calibration Fault detected in calibration User-dependentcalibration (factory) On-demand information Gui increased, entry intoRegular or periodic pushed glycemic danger zone, entry information intohypo/hyper states Regular or periodic Gui decreased, exit from On-demandinformation pushed information glycemic danger zone, exit fromhypo/hyper states Adjunctive Increasing Usability Of Data, TherapeuticIncreasing Confidence In Data, User Desire For Therapeutic SupportTherapeutic Decreased Signal Usability, Adjunctive Fault Detected, UserChoice Adjunctive & user- Increased signal usability & Therapeutic mode,device dependent calibration increased confidence in self-calibrationmode, or both modes calibration On-demand information Fault, decrease insignal Pushed information, user- & device self-calibration usability,entry into glycemic dependent calibration, or both danger zone,increased gui, entry into hypo/hyper states Phase 6 Analyte sensor (notinsulin) fail Phase 5 Phase 6 All analyte sensors decrease in Phase 0signal or data usability Phase 5, 4, 3, 2, 1 Glucose sensor decreases inPhase 0 signal or data usability Phase 5 User detection of perceivedPhase 4 errors Phase 4 Decrease in signal usability Phase 3, 2, 1, or 0(depending on signal usability) Phase 0 Increase in signal usability toPhase 1 point where analyte concentration value is trusted but notpredictive value

This process responsiveness (of mode or phase to criteria such as signalusability) may be implemented in some cases by causing differentcommands to be sent to an insulin pump controller. For example, andreferring to the flowchart 475 of FIG. 16C, in a mode or phase that isat least partially therapeutic (step 430), pump control may be exertedat least partially under control of the monitoring device (step 436).And as noted if data usability becomes very low, the monitoring devicemay be caused to transition to an adjunctive mode (step 432) where themonitoring device does not control the pump.

According to, e.g., the sequence of FIG. 15A, various levels of pumpcontrol may be exerted short of full pump control. Alternatively, evenif data usability is high, a user or clinician may choose to exert, orthe monitoring device may be configured to switch to a mode exerting, avarying or reduced level of pump control.

The levels of pump control exerted in step 436 may be considered toselect an aggressiveness of the pump control. Specific examples of suchaggressiveness levels are now described.

A number of actions are described within FIG. 16C, and many of these aredescribed as “do or do not” perform a given step, e.g., treathypoglycemia, control to target, calculate bolus, etc. As part of aprocedure within phase 0, the “do not” portions of these steps may begenerally employed, e.g., do not allow hypo/hyper minimizer (step 438),do not suspend for low glucose (step 440), do not calculate bolus (step442), do not treat hyperglycemia/hypoglycemia (step 444), do not controlto range (step 447), and do not control to target (step 448).

The “do” portions of the steps may then be enabled to perform one ormore functions within a system for phased therapeutic control, e.g., asnoted with regard to FIG. 15A. For example, to accomplish phase 1, thesystem may be configured to suspend pump actions for low glucoseconcentration values (step 440). To accomplish phase 2, a level ofprediction may be enabled, and alarms may be caused if hypoglycemia ispredicted. The same may be accompanied by a reduction or cessation ofinsulin below a predetermined threshold.

To accomplish phase 3, the system may be configured to allow a“hypo-hyper minimizer” (step 438), where the same is a closed-loopsystem that only controls insulin using sensor data at low and highglucose levels, rather than using the same also within target ranges. Toaccomplish phase 4, the system may be configured to calculate a bolus(step 442) during mealtime. In this way the system acts in many waysclosed loop but allows additional user control during particularlyuncontrolled times, e.g., meal times area

To accomplish phase 5, the system made more generally be configuredwithin to treat hyperglycemia and hypoglycemia, e.g., by dosing insulinand, e.g., glucagon, as appropriate. Finally, to accomplish phase 6,other hormones may be employed to provide even greater balance andstability to the patient's biochemistry. In so doing, the system maycontrol to a target value of the hormone (step 448), or alternatively toa target range (step 447).

Within these phases, it will be understood that the exerted level ofpump control may vary. For example, closed-loop control such asavailable in phases 4-6 may in certain implementations be used onlywithin certain glucose ranges, e.g., 20-70 mg/dL. Ranges may beindividually-controlled based on certain criteria and whether the modeis adjunctive or therapeutic or based on the phase. For example,depending on the evaluation step, there could be different criteria inadjunctive mode for treating hyperglycemia versus hypoglycemia.

FIG. 17 is a flowchart 500 illustrating processing and display changeswhich may be caused or performed when mode switching (step 456) occursbetween modes. For simplicity, FIG. 17 contemplates a therapeutic mode452 and an adjunctive mode 454, although similar UI aspects can beextended to other types of mode switching as well as switching betweenphases as described above. Generally, the way in which data is displayedon the user interface may be changed to indicate at least in part theusability of the data (step 462).

In one example, an actual glucose concentration value may be selected tobe shown versus a zone of glucose concentration values (step 464). Thissituation is also illustrated in FIGS. 18A and 18B, where the figuresshow a monitoring device 560 having a glycemic range 562 illustrated bya color indication, as well as an indicator 564 of a trend, in FIG. 18,a downward trend. In FIG. 18A, a numerical value 566 is illustrated,showing a glucose concentration value of 75 mg/dL, while in FIG. 18B, nosuch value is displayed. In the former, the confidence level orusability of the data satisfies a criterion, e.g., is greater than athreshold level, allowing the data to be illustrated. In the latter, theconfidence level or usability has not met the criterion, e.g., is belowthe threshold, and thus display of the numerical value is suppressed. Inboth cases, the confidence level or usability of the data about theglycemic range, as well as the rate of change, satisfies the criterion,e.g., is higher than the threshold, and thus this data is present onboth exemplary user interfaces.

Referring back to FIG. 17, in another implementation, different levelsof advice or help, e.g., such as displayed help from an avatar or otheron-screen display, may be provided to a user (step 466). For example, ina therapeutic mode in which a bolus may be enabled to be calculated, anavatar may inform the user about an amount of insulin directed to bepumped. In a mode employed for educational purposes, an avatar mayprovide not only glucose data, but a significant level of suggestion andinformation about a current glycemic range, or other data which may beuseful to a user in developing a knowledge of CGM operation and diseasemanagement.

Additional details of systems and methods for use of avatars aredisclosed in US Patent Publication No. 2010/0261987-A1; and US PatentPublication No. 2014/0184422-A1, both of which are owned by the assigneeof the present application and herein incorporated by reference in theirentireties.

In yet another implementation, an avatar may be employed to appear andinform the user about how they should be using their device (step 478).In an implementation, a user may be required to acknowledge receipt ofsuch data before viewing glucose data again. Such an implementation maybe particularly advantageous where it can be deduced that a user is notusing their device correctly or is not consulting the same frequentlyenough.

In another implementation, colors or other visual effects may beemployed to indicate mode and/or usability of determined data (step468). For example, and referring to FIGS. 18C and 18D, FIG. 18C shows amonitoring device 560 in which one mode is indicated by a widely spacedvertical line pattern 568. FIG. 18D shows the same monitoring device 560in which a different mode has been entered, and the same indicated by adifferent (more narrowly spaced) vertical line pattern 572. In bothcases, a common glucose reading 570 is displayed on the backgroundpattern. Using this implementation, a user can quickly and visuallyidentify the mode their device is in. Patterns may be implemented whichrepresent multiple modes as well. For example, a pattern may represent atherapeutic mode in which user-dependent calibration is performed whileanother pattern may represent that an adjunctive mode is operating inwhich device self-calibration is employed. Other combinations andimplementations will also be understood given this teaching, as isdescribed above and in applications incorporated by reference.

The level of data usability may be indicated in other ways as well,e.g., by the altering of a data indicator on a user interface toindicate a level of data usability. In one example, the displayedglucose concentration values themselves may be caused to flash or besteady to indicate usability, e.g., flashing data may be associated witha lower usability, e.g., may have less reliability or a lower confidencelevel, than steadily-rendered data. Another way to indicate usability byaltering a data indicator includes altering another aspect of a chartdisplay (step 472), as shown in the inset chart 475 in FIG. 17, in whichpoints 477 i represent glucose concentration values on an axis 471 andwhich are plotted against time on axis 473. The size of a dotrepresenting glucose on the trend graph may be made larger when itsusability is less, e.g., where its corresponding data is of a lowerconfidence. In this way the dot size indicates range information, i.e.,the size of the dot can represent the range. Such may then show adifference between more usable (in this case reliable) data having avalue of, e.g., 78 mg/dL, versus less usable data, having a value withina range of 60-90 mg/dL. Other variations will also be understood,including the use of large dots to indicate greater data usability.

Another method to indicate data usability on a user interface is to showranges versus numbers in a selective fashion as shown by the chart 525of FIG. 19. In this chart, certain data 492 are illustrated by dotswhile other data 490 are illustrated by ranges of values in a waysimilar to error bars. The ranges and dots are indicated on the x-axisas being of high data usability, e.g., in a high confidence range 484and 488 (the dots 492), or as being of low data usability, e.g., in alow confidence range 486 (the range bars 490).

In another implementation, a subset of data may be displayed (step 470)in order to indicate, e.g., a lack of confidence in certain un-displayedinformation. In other words, a restricted information display may beused to show, e.g., a rate of change arrow, but not an actual glucoseconcentration value. In this way, data is not displayed unless itsatisfies a criterion for usability. Such an implementation may besimilar to the information displayed on the monitoring devices of FIGS.18A and 18B.

Referring back to FIG. 17, in yet another implementation, a change inthe prediction horizon may be displayed where the same depends on theusability of the signal (step 476). The change in prediction horizon mayinclude whether the device employs a prediction horizon, whether thedevice displays a prediction, as well as whether alerts or alarms areemployed based on such predictions. In particular, the predictionhorizon may be varied from 10, 20, or 30 minutes in the future,depending on the confidence of the prediction, which may be affected byusability-related parameters and variables such as signal quality,confidence in a calibration, confidence in resulting data, acceleration,or the like.

In the particular case of the adjunctive mode, the display may continueto show glucose concentration values, but an alert or alarm screen couldindicate a warning that external meter values should be used to confirmCGM values prior to dosing (step 482).

In one specific example, an insulin bolus calculator on the monitoringdevice may run in an adjunctive mode or therapeutic mode based on therelative confidence in the CGM data, which in turn can be part of atransition criterion or criteria. For example, if the confidence isabove a threshold, then the bolus calculator can determine a recommendedinsulin bolus without input from an external meter (step 460). However,if the confidence is below the threshold, then the bolus calculator mayrequest external meter reference values for use in bolus estimation.

In another specific example, an insulin pump may be programmed withbasal estimates by the user, while bolus estimates may be entered by theuser. In an adjunctive mode, CGM data may be used to make suggestions(step 480), such as “bolus may be too high”, or “may need a differenttype of bolus”. In therapeutic mode, which may also depend on phase, CGMdata may be employed to modify the basal or bolus estimates, with orwithout user confirmation (step 458).

In another specific example, as shown by the flowchart 550 of FIG. 20,startup of the monitoring device may occur immediately after sensorinsertion (step 494), e.g., within 15 minutes or the like, andlow-resolution data may be immediately displayed (step 496). This is incontrast to many present systems in which sensors do not start up until,e.g., two hours, after insertion. Low-resolution data may be displayedthat includes, e.g., a glycemic range the patient is in, glucose rangeinformation, or other data which may be considered adjunctive-mode-typedata. Over time, a confidence level or other indicator of the usabilityof the data may rise (step 498). This rise may occur in a number ofways. For example, an external meter value may be provided (step 508)that either correlates to what the CGM estimates the glucoseconcentration value to be and/or the external meter value entered maytrigger a mode switch to a user-dependent calibration mode (step 510),and if the usability is high enough, a therapeutic mode may be entered(step 506). The usability may also dictate the phase in which the deviceis operating, if such phases are enabled (see FIG. 15A). In anotheralternative, an increase may occur in a level of certainty in the deviceself-calibration mode (step 502) sufficiently so as to display glucoseconcentration values at a higher resolution (step 504), and thus justifya transition if desired by the user into a therapeutic mode or phase(step 506).

In other words, an adjunctive mode is maintained until external meterdata indicates a mode switch to user-dependent calibration, at whichpoint sufficient resolution may be achieved to perform a further modeswitch of the adjunctive mode to the therapeutic mode. Correspondingly,a criteria for determining the calibration mode, e.g., whether or not touse user-dependent calibration, may be based on whether or not thetherapeutic mode versus the adjunctive mode has been triggered. Forexample, if the therapeutic mode has been triggered, it may be desirableto switch to a user-dependent calibration mode. Similarly, if theadjunctive mode has been triggered, it may be desirable to switch to adevice self-calibration mode if calibration parameters allow. In thesefashions, FIG. 20 illustrates an interplay between the calibration modeand the therapeutic/adjunctive use modes, as well as their potentialeffects on each other.

As one example, signal usability of a monitoring device may cause thesame to be operating in a user-dependent calibration mode. For example,the monitoring device may be registering a fault in the sensor due to ashower spike. However, if the fault is resolved, and confidence rises inthe device self-calibration, at some point the device self-calibrationmay be allowed to “take over” and reduce the need for user-dependentcalibration. At the same time, if confidence also rises in the signalusability, the decision support mode may be caused to transition from anadjunctive one to a therapeutic one, or in the case where phases ofcontrol are implemented, to a higher control phase.

In another specific example, as shown by the diagram 575 of FIG. 21A andthe flowchart 600 of FIG. 21B, adjunctive and therapeutic modes may beoperated in parallel, simultaneously, or concurrently, where a basicscreen 512 is provided that shows low-resolution data, e.g., trends,arrows, and/or ranges. Such data may be sufficient for adjunctive useand require little or no calibration. The user can then select otherscreens via a menu, and in some cases such other screens such as acomplex screen 514 will provide an alert or alarm to the user thatadditional and higher levels of data usability, e.g., calibration oraccuracy, are required, in which case the more complex or therapeuticmodes (running in parallel, simultaneously, or concurrently) may beswitched to if the usability of the data allows.

There could be different levels of information provided by the screens,which, when selected, would trigger different requirements/criteria indifferent submodes, based on what the user wants to see or do. Forexample, a basic information screen 516 may provide a simple arrowindicating rate of change as well as a glycemic range. A hypoglycemiasafety screen 518 may be provided that alarms on hypoglycemia only. Sucha screen may require some degree of calibration, including potentiallyuser-dependent calibration. A full management information screen 520 mayalso be provided, which alerts and alarms for high and low values aswell as high or low rates of change. This type of screen display mayrequire even more signal usability than the previously described screens(which may require more or greater calibration in some circumstances).If the required levels of data resolution become high enough, the modemay switch to another mode, as noted above, e.g., the mode may switch tothe therapeutic mode if the user has indicated a desire for such aswitch. This system allows flexibility in mode switching—the user isenabled to view a high resolution mode but is not required to do so andin fact can employ a simple UI even with data that is innately of highresolution. The parallel modes, which as noted may run concurrently orsimultaneously, need not run concurrently or simultaneously for anentire duration of a session. But while they are running in parallel,modes may be easily switched from one to another. Of course, when theyare not running in parallel, the modes may be switched using any of theother methods described herein, e.g., with respect to FIGS. 1, 3A, 3B,4, 9-15A, 15C, 17, or 20.

As seen in the flowchart 600, in a first step a basic screen may beprovided on the user interface of the monitoring device for adjunctiveuse (step 522). Options may then be provided for additional uses (step524), where such options are provided on the user interface of themonitoring device. If the user selects an information screen whichrequires additional information (step 526), the monitoring device mayeither automatically switch to a mode in which sufficient calibration orother data are received to enable the additional information, or theuser may be prompted before such modes are entered. Once such data isreceived by the monitoring device, the additional uses may be enabled(step 528), and the screen with additional information displayed.

In another specific example, as shown by the flowchart 625 of FIG. 22,the monitoring device may identify hypoglycemia unawareness in a user(step 530), such as by a lack of correlation between a user feeling lowand CGM data actually being low. In this case, a mode switch may occurto a mode that is more sensitive (step 532). In an optional step,accuracy criteria may also be modified (step 534). As a result of themode switch, the monitoring device may output and display moreinformation for the user (step 536), especially where the device is intherapeutic mode. The additional information may further educate theuser as to how to identify hypoglycemia, how to treat the same, and theramifications of the hypoglycemic state.

Overview/General Description of System

The glucose sensor can use any system or method to provide a data streamindicative of the concentration of glucose in a host. The data stream istypically a raw data signal that is transformed to provide a usefulvalue of glucose to a user, such as a patient or doctor, who may beusing the sensor. Faults may occur, however, which may be detectable byanalysis of the signal, analysis of the clinical context, or both. Suchfaults require discrimination to distinguish the same from actualmeasured signal behavior, as well as for responsive signal processing,which can vary according to the fault. Accordingly, appropriate faultdiscrimination and responsive processing techniques are employed.

Glucose Sensor

The glucose sensor can be any device capable of measuring theconcentration of glucose. One exemplary embodiment is described below,which utilizes an implantable glucose sensor. However, it should beunderstood that the devices and methods described herein can be appliedto any device capable of detecting a concentration of glucose andproviding an output signal that represents the concentration of glucose.

Exemplary embodiments disclosed herein relate to the use of a glucosesensor that measures a concentration of glucose or a substanceindicative of the concentration or presence of another analyte. In someembodiments, the glucose sensor is a continuous device, for example asubcutaneous, transdermal, transcutaneous, non-invasive, intraocularand/or intravascular (e.g., intravenous) device. In some embodiments,the device can analyze a plurality of intermittent blood samples. Theglucose sensor can use any method of glucose measurement, includingenzymatic, chemical, physical, electrochemical, optical, optochemical,fluorescence-based, spectrophotometric, spectroscopic (e.g., opticalabsorption spectroscopy, Raman spectroscopy, etc.), polarimetric,calorimetric, iontophoretic, radiometric, and the like.

The glucose sensor can use any known detection method, includinginvasive, minimally invasive, and non-invasive sensing techniques, toprovide a data stream indicative of the concentration of the analyte ina host. The data stream is typically a raw data signal that is used toprovide a useful value of the analyte to a user, such as a patient orhealth care professional (e.g., doctor), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor capable of measuring the concentration of glucose in a host, thesystems and methods of embodiments can be applied to any measurableanalyte, a non-exhaustive list of appropriate analytes noted above. Someexemplary embodiments described below utilize an implantable glucosesensor. However, it should be understood that the devices and methodsdescribed herein can be applied to any device capable of detecting aconcentration of analyte and providing an output signal that representsthe concentration of the analyte.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1. In anotherpreferred embodiment, the analyte sensor is a transcutaneous glucosesensor, such as described with reference to U.S. Patent Publication No.US-2006-0020187-A1. In still other embodiments, the sensor is configuredto be implanted in a host vessel or extracorporeally, such as isdescribed in U.S. Patent Publication No. US-2007-0027385-A1, co-pendingU.S. patent application Ser. No. 11/543,396 filed Oct. 4, 2006,co-pending U.S. patent application Ser. No. 11/691,426 filed on Mar. 26,2007, and co-pending U.S. patent application Ser. No. 11/675,063 filedon Feb. 14, 2007. In one alternative embodiment, the continuous glucosesensor comprises a transcutaneous sensor such as described in U.S. Pat.No. 6,565,509 to Say et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises a subcutaneoussensor such as described with reference to U.S. Pat. No. 6,579,690 toBonnecaze et al. or U.S. Pat. No. 6,484,046 to Say et al., for example.In another alternative embodiment, the continuous glucose sensorcomprises a refillable subcutaneous sensor such as described withreference to U.S. Pat. No. 6,512,939 to Colvin et al., for example. Inanother alternative embodiment, the continuous glucose sensor comprisesan intravascular sensor such as described with reference to U.S. Pat.No. 6,477,395 to Schulman et al., for example. In another alternativeembodiment, the continuous glucose sensor comprises an intravascularsensor such as described with reference to U.S. Pat. No. 6,424,847 toMastrototaro et al.

Various sensors may be employed. In the case of continuous glucosesensing, it is contemplated that the sensing region may include any of avariety of electrode configurations. For example, in some embodiments,in addition to one or more glucose-measuring working electrodes, thesensing region may also include a reference electrode or otherelectrodes associated with the working electrode. In these particularembodiments, the sensing region may also include a separate reference orcounter electrode associated with one or more optional auxiliary workingelectrodes. In other embodiments, the sensing region may include aglucose-measuring working electrode, an auxiliary working electrode, twocounter electrodes (one for each working electrode), and one sharedreference electrode. In yet other embodiments, the sensing region mayinclude a glucose-measuring working electrode, an auxiliary workingelectrode, two reference electrodes, and one shared counter electrode.

U.S. Patent Publication No. US-2008-0119703-A1 and U.S. PatentPublication No. US-2005-0245799-A1 describe additional configurationsfor using the continuous sensor in different body locations. In someembodiments, the sensor is configured for transcutaneous implantation inthe host. In alternative embodiments, the sensor is configured forinsertion into the circulatory system, such as a peripheral vein orartery. However, in other embodiments, the sensor is configured forinsertion into the central circulatory system, such as but not limitedto the vena cava. In still other embodiments, the sensor can be placedin an extracorporeal circulation system, such as but not limited to anintravascular access device providing extracorporeal access to a bloodvessel, an intravenous fluid infusion system, an extracorporeal bloodchemistry analysis device, a dialysis machine, a heart-lung machine(i.e., a device used to provide blood circulation and oxygenation whilethe heart is stopped during heart surgery), etc. In still otherembodiments, the sensor can be configured to be wholly implantable, asdescribed in U.S. Pat. No. 6,001,067.

FIG. 23 is a block diagram that illustrates one possible configurationof the sensor electronics in one embodiment. In this embodiment, apotentiostat 720 is shown, which is operatively connected to anelectrode system and provides a voltage to the electrodes, which biasesthe sensor to enable measurement of a current value indicative of theanalyte concentration in the host (also referred to as the analogportion). In some embodiments, the potentiostat includes a resistor (notshown) that translates the current into voltage. In some alternativeembodiments, a current to frequency converter is provided that isconfigured to continuously integrate the measured current, for example,using a charge counting device. In the illustrated embodiment, an A/Dconverter 721 digitizes the analog signal into “counts” for processing.Accordingly, the resulting raw data stream in counts is directly relatedto the current measured by the potentiostat 720.

A processor module 722 is the central control unit that controls theprocessing of the sensor electronics. In some embodiments, the processormodule includes a microprocessor, however a computer system other than amicroprocessor can be used to process data as described herein, forexample an ASIC can be used for some or all of the sensor's centralprocessing. The processor typically provides semi-permanent storage ofdata, for example, storing data such as sensor identifier (ID) andprogramming to process data streams (for example, programming for datasmoothing and/or replacement of signal artifacts such as is described inmore detail elsewhere herein). The processor additionally can be usedfor the system's cache memory, for example for temporarily storingrecent sensor data. In some embodiments, the processor module comprisesmemory storage components such as various types of ROM, RAM, flashmemory, and the like. In one exemplary embodiment, ROM 723 providessemi-permanent storage of data, for example, storing data such as sensoridentifier (ID) and programming to process data streams (e.g.,programming for signal artifacts detection and/or replacement such asdescribed elsewhere herein). In one exemplary embodiment, RAM 724 can beused for the system's cache memory, for example for temporarily storingrecent sensor data.

In some embodiments, the processor module comprises a digital filter,for example, an IIR or FIR filter, configured to smooth the raw datastream from the A/D converter. Generally, digital filters are programmedto filter data sampled at a predetermined time interval (also referredto as a sample rate). In some embodiments, wherein the potentiostat isconfigured to measure the analyte at discrete time intervals, these timeintervals determine the sample rate of the digital filter. In somealternative embodiments, wherein the potentiostat is configured tocontinuously measure the analyte, for example, using acurrent-to-frequency converter, the processor module can be programmedto request a digital value from the A/D converter at a predeterminedtime interval, also referred to as the acquisition time. In thesealternative embodiments, the values obtained by the processor areadvantageously averaged over the acquisition time due the continuity ofthe current measurement. Accordingly, the acquisition time determinesthe sample rate of the digital filter. In preferred embodiments, theprocessor module is configured with a programmable acquisition time,namely, the predetermined time interval for requesting the digital valuefrom the A/D converter is programmable by a user within the digitalcircuitry of the processor module. An acquisition time of from about 2seconds to about 512 seconds is preferred; however any acquisition timecan be programmed into the processor module. A programmable acquisitiontime is advantageous in optimizing noise filtration, time lag, andprocessing/battery power.

Preferably, the processor module is configured to build the data packetfor transmission to an outside source, for example, an RF transmissionto a receiver as described in more detail below. Generally, the datapacket comprises a plurality of bits that can include a sensor ID code,raw data, filtered data, and/or error detection or correction. Theprocessor module can be configured to transmit any combination of rawand/or filtered data.

A battery 725 is operatively connected to the processor 722 and providesthe necessary power for the sensor (e.g., 800). In one embodiment, thebattery is a Lithium Manganese Dioxide battery, however anyappropriately sized and powered battery can be used (e.g., AAA,Nickel-cadmium, Zinc-carbon, Alkaline, Lithium, Nickel-metal hydride,Lithium-ion, Zinc-air, Zinc-mercury oxide, Silver-zinc, orhermetically-sealed). In some embodiments the battery is rechargeable.In some embodiments, a plurality of batteries can be used to power thesystem. In yet other embodiments, the receiver can be transcutaneouslypowered via an inductive coupling, for example. A Quartz Crystal 726 isoperatively connected to the processor 22 and maintains system time forthe computer system as a whole.

An RF module, (e.g., an RF Transceiver) 727 is operably connected to theprocessor 722 and transmits the sensor data from the sensor (e.g., 800)to a receiver (see FIGS. 27 and 28). Although an RF transceiver is shownhere, some other embodiments can include a wired rather than wirelessconnection to the receiver. A second quartz crystal 728 provides thesystem time for synchronizing the data transmissions from the RFtransceiver. It is noted that the transceiver 727 can be substitutedwith a transmitter in other embodiments. In some alternativeembodiments, however, other mechanisms, such as optical, infraredradiation (IR), ultrasonic, and the like, can be used to transmit and/orreceive data.

In some embodiments, a Signal Artifacts Detector 729 is provided thatincludes one or more of the following: an oxygen detector 729 a, a pHdetector 729 b, a temperature detector 729 c, and a pressure/stressdetector 729 d, which is described in more detail with reference tosignal artifacts and faults/errors detection and discrimination. It isnoted that in some embodiments the signal artifacts detector 729 is aseparate entity (e.g., temperature detector) operatively connected tothe processor, while in other embodiments, the signal artifacts detectoris a part of the processor and utilizes readings from the electrodes,for example, to detect signal faults and artifacts. Although the abovedescription includes some embodiments in which all discrimination occurswithin the sensor, other embodiments provide for systems and methods fordetecting signal faults in the sensor and/or receiver electronics (e.g.,processor module) as described in more detail elsewhere herein.

Receiver

FIG. 24 is a schematic view of a receiver 730 including a representationof an estimated glucose value on its user interface. The receiver 730includes systems to receive, process, and display sensor data from theglucose sensor (e.g., 800), such as described herein. Particularly, thereceiver 730 can be a mobile phone type device, for example, andcomprise a user interface that has a physical button 732 and a displayscreen 734, as well as one or more input/output (I/O) devices, such as atouch screen, one or more buttons 755 and/or switches 757, which whenactivated or clicked perform one or more functions. In FIG. 24, the UIalso shows historical trend data, as well as a compass-type icon elementsurrounding the glucose concentration value indicating a rate-of-changetrend. Various other features are shown, including UI icons which may beemployed to enter medicament information, e.g., insulin boluses,exercise data, and to provide social networking functionality.

In the illustrated embodiment, the electronic device is a smartphone,and the display 734 comprises a touchscreen, which also functions as anI/O device. In some embodiments, the user interface can also include akeyboard, a speaker, and a vibrator. The functions of the receiver orsmart phone can also be implemented as functions within an applicationrunning on a tablet computer, or like device. In other embodiments, thereceiver may comprise a device or devices other than a smartphone, suchas a smartwatch, a tablet computer, a mini-tablet computer, a handheldpersonal digital assistant (PDA), a game console, a multimedia player, awearable device, such as those described above, a screen in anautomobile or other vehicle, a dedicated receiver device, etc. Thereceiver may also be a medicament administration device such as aninsulin pump.

In some embodiments, the user will be able to interactively select thetype of output displayed on their user interface. In other embodiments,the sensor output can have alternative configurations. In yet otherembodiments, the type of output displayed on the user interface willdisplay on the mode switched to, e.g., indicating an operating mode, atype of user interaction, data appropriate thereto, and/or the like.

FIG. 25 is a block diagram that illustrates one possible configurationof the receiver, e.g., a smart phone, electronics. It is noted that thereceiver can comprise a configuration such as described with referenceto FIG. 24, above. Alternatively, the receiver can comprise otherconfigurations, including a desktop computer, laptop computer, apersonal digital assistant (PDA), a server (local or remote to thereceiver), and the like. In some embodiments, the receiver can beadapted to connect (via wired or wireless connection) to a desktopcomputer, laptop computer, PDA, server (local or remote to thereceiver), and the like, in order to download data from the receiver. Insome alternative embodiments, the receiver and/or receiver electronicscan be housed within or directly connected to the sensor (e.g., 800) ina manner that allows sensor and receiver electronics to work directlytogether and/or share data processing resources. Accordingly, thereceiver's electronics (or any combination of sensor and/or receiverelectronics) can be generally referred to as a “computer system.”

A quartz crystal 740 is operatively connected to an RF transceiver 741that together function to receive and synchronize data streams (e.g.,raw data streams transmitted from the RF transceiver). Once received, aprocessor 742 processes the signals, such as described below.

The processor 742, also referred to as the processor module, is thecentral control unit that performs the processing, such as comparingdetermined data against criteria to determine if mode switching shouldoccur, storing data, analyzing data streams, calibrating analyte sensordata, predicting analyte values, comparing predicted analyte values withcorresponding measured analyte values, analyzing a variation ofpredicted analyte values, downloading data, and controlling the userinterface by providing analyte values, prompts, messages, warnings,alarms, and the like. The processor includes hardware and software thatperforms the processing described herein, for example flash memoryprovides permanent or semi-permanent storage of data, storing data suchas sensor ID, receiver ID, and programming to process data streams (forexample, programming for performing prediction and other algorithmsdescribed elsewhere herein) and random access memory (RAM) stores thesystem's cache memory and is helpful in data processing.

In one exemplary embodiment, the processor is a microprocessor thatprovides the processing, such as calibration algorithms stored within aROM 743. The ROM 743 is operatively connected to the processor 742 andprovides semi-permanent storage of data, storing data such as receiverID and programming to process data streams (e.g., programming forperforming calibration and other algorithms described elsewhere herein).In this exemplary embodiment, a RAM 744 is used for the system's cachememory and is helpful in data processing.

A battery 745 is operatively connected to the processor 742 and providespower for the receiver. In one embodiment, the battery is a standard AAAalkaline battery, however any appropriately sized and powered batterycan be used. In some embodiments, a plurality of batteries can be usedto power the system. A quartz crystal 746 is operatively connected tothe processor 742 and maintains system time for the computer system as awhole.

A user interface 747 comprises a keyboard 2, speaker 3, vibrator 4,backlight 5, liquid crystal display (LCD 6), and one or more buttons 7,which may be implemented as physical buttons or buttons on a touchscreeninterface. The components that comprise the user interface 47 providecontrols to interact with the user. The keyboard 2 can allow, forexample, input of user information about himself/herself, such asmealtime, exercise, insulin administration, and reference glucosevalues. The speaker 3 can provide, for example, audible signals oralerts for conditions such as present and/or predicted hyper- andhypoglycemic conditions. The vibrator 4 can provide, for example,tactile signals or alerts for reasons such as described with referenceto the speaker, above. The backlight 5 can be provided, for example, toaid the user in reading the LCD in low light conditions. The LCD 6 canbe provided, for example, to provide the user with visual data outputsuch as is illustrated in FIG. 24. The buttons 7 can provide for toggle,menu selection, option selection, mode selection, and reset, forexample.

In some embodiments, prompts or messages can be displayed on the userinterface to convey information to the user, such as requests forreference analyte values, therapy recommendations, deviation of themeasured analyte values from the predicted analyte values, and the like.Additionally, prompts can be displayed to guide the user throughcalibration or trouble-shooting of the calibration.

In some implementations, the continuous analyte sensor system includes aDexcom G4® Platinum glucose sensor and transmitter commerciallyavailable from Dexcom, Inc., for continuously monitoring a host'sglucose levels.

In some embodiments, the system may execute various applications, forexample, a CGM application, which may be downloaded to the receiver orother electronic device over the Internet and/or a cellular network, andthe like. Data for various applications may be shared between the deviceand one or more other devices/systems, and stored by cloud or networkstorage and/or on one or more other devices/systems.

What has been disclosed are systems and methods for dynamically anditeratively changing or switching modes and/or submodes of a monitoringdevice based on determined data, generally compared against a triggersuch as one or more transition criteria. A variety of methods have beendisclosed for determining when and how to switch modes, as well as anumber of potential types of data and criteria.

Variations will be understood to one of ordinary skill in the art giventhis teaching. For example, while multimodal transitions have beendescribed, it will be understood that such may include multipletransition criteria from one category or multiple transition criteriadrawn from multiple categories, e.g., from multiple of flowcharts fromFIGS. 5-9.

The connections between the elements shown in the figures illustrateexemplary communication paths. Additional communication paths, eitherdirect or via an intermediary, may be included to further facilitate theexchange of information between the elements. The communication pathsmay be bi-directional communication paths allowing the elements toexchange information.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

In one or more aspects, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over as oneor more instructions or code on a computer-readable medium.Computer-readable media includes both computer storage media andcommunication media including any medium that facilitates transfer of acomputer program from one place to another. A storage media may be anyavailable media that can be accessed by a computer. By way of example,and not limitation, such computer-readable media can comprise RAM, ROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Also, any connectionis properly termed a computer-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio, and microwave areincluded in the definition of medium. Disk and disc, as used herein,includes compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk and Blu-ray® disc where disks usually reproducedata magnetically, while discs reproduce data optically with lasers.Thus, in some aspects a computer-readable medium may comprisenon-transitory computer-readable medium (e.g., tangible media). Inaddition, in some aspects a computer-readable medium may comprisetransitory computer-readable medium (e.g., a signal). Combinations ofthe above should also be included within the scope of computer-readablemedia.

The methods disclosed herein comprise one or more steps or actions forachieving the described methods. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isspecified, the order and/or use of specific steps and/or actions may bemodified without departing from the scope of the claims.

Certain aspects may comprise a computer program product for performingthe operations presented herein. For example, such a computer programproduct may comprise a computer-readable medium having instructionsstored (and/or encoded) thereon, the instructions being executable byone or more processors to perform the operations described herein. Forcertain aspects, the computer program product may include packagingmaterial.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained. For example, a device can becoupled to a server to facilitate the transfer of means for performingthe methods described herein. Alternatively, various methods describedherein can be provided via storage means (e.g., RAM, ROM, a physicalstorage medium such as a compact disc (CD) or floppy disk, etc.), suchthat a user terminal and/or base station can obtain the various methodsupon coupling or providing the storage means to the device. Moreover,any other suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit and each intervening value between the upper and lower limitof the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention, e.g., as including any combination ofthe listed items, including single members (e.g., “a system having atleast one of A, B, and C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). In those instanceswhere a convention analogous to “at least one of A, B, or C, etc.” isused, in general such a construction is intended in the sense one havingskill in the art would understand the convention (e.g., “a system havingat least one of A, B, or C” would include but not be limited to systemsthat have A alone, B alone, C alone, A and B together, A and C together,B and C together, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

The system and method may be fully implemented in any number ofcomputing devices. Typically, instructions are laid out oncomputer-readable media, generally non-transitory, and theseinstructions are sufficient to allow a processor in the computing deviceto implement the method of the invention. The computer-readable mediummay be a hard drive or solid state storage having instructions that,when run, are loaded into random access memory. Inputs to theapplication, e.g., from the plurality of users or from any one user, maybe by any number of appropriate computer input devices. For example,users may employ a keyboard, mouse, touchscreen, joystick, trackpad,other pointing device, or any other such computer input device to inputdata relevant to the calculations. Data may also be input by way of aninserted memory chip, hard drive, flash drives, flash memory, opticalmedia, magnetic media, or any other type of file—storing medium. Theoutputs may be delivered to a user by way of a video graphics card orintegrated graphics chipset coupled to a display that maybe seen by auser. Alternatively, a printer may be employed to output hard copies ofthe results. Given this teaching, any number of other tangible outputswill also be understood to be contemplated by the invention. Forexample, outputs may be stored on a memory chip, hard drive, flashdrives, flash memory, optical media, magnetic media, or any other typeof output. It should also be noted that the invention may be implementedon any number of different types of computing devices, e.g., personalcomputers, laptop computers, notebook computers, net book computers,handheld computers, personal digital assistants, mobile phones, smartphones, tablet computers, and also on devices specifically designed forthese purpose. In one implementation, a user of a smart phone orwi-fi—connected device downloads a copy of the application to theirdevice from a server using a wireless Internet connection. Anappropriate authentication procedure and secure transaction process mayprovide for payment to be made to the seller. The application maydownload over the mobile connection, or over the WiFi or other wirelessnetwork connection. The application may then be run by the user. Such anetworked system may provide a suitable computing environment for animplementation in which a plurality of users provide separate inputs tothe system and method. In the below system where mode switching iscontemplated, the plural inputs may allow plural users or devices toinput relevant data and criteria at the same time.

Various methods and devices are provided.

In Method 1, provided is a method of operating a continuous glucosemonitoring device, the continuous glucose monitoring device coupled to aglucose sensor and operating in an initial mode of operation,comprising: measuring a signal indicative of glucose concentration data;displaying the glucose concentration data on a user interface of thecontinuous glucose monitoring device, the user interface in the initialmode of operation having an initial mode of user interaction;determining data indicative of a usability of the continuous glucosemonitoring device; comparing the determined data to one or moretransition criteria; if the comparing indicates the determined data hasmet or will meet the transition criteria, causing the continuous glucosemonitoring device to transition to a new mode of operation; anddisplaying the glucose concentration data on the user interface of thecontinuous glucose monitoring device, the user interface in the new modeof operation having a different mode of user interaction than theinitial mode, such that the continuous glucose monitoring deviceoperates in a mode of user interaction according to the deviceusability.

In Method 2, which is a variant of Method 1, the displaying is based atleast in part on the mode of operation.

In Method 3, which is a variant of Method 1 or 2, the determining dataincludes receiving data from the sensor.

In Method 4, which is a variant of Method 3, the receiving data from thesensor includes receiving data from a sensor electronics module coupledto the sensor.

In Method 5, which is a variant of any one of the above Methods, thesensor is configured for in vivo insertion into the patient.

In Method 6, which is a variant of any one of the above Methods, a firstoutput of the monitoring device in the initial mode of operationrepresents the initial mode of user interaction and a second output ofthe monitoring device in the new mode of operation represents the newmode of user interaction, and wherein the first and second outputs aredifferent.

In Method 7, which is a variant of Method 6, the initial and new modesof user interaction are configured such that the new mode of userinteraction requires less user interaction than the initial mode of userinteraction.

In Method 8, which is a variant of Method 8, the initial and new modesof user interaction are selected from the group consisting of:user-dependent calibration and device self-calibration.

In Method 9, which is a variant of Method 8, the analyte is glucose andwherein the user-dependent calibration corresponds to entry of acalibration value from an external blood glucose meter.

In Method 10, which is a variant of Method 6, the initial and new modesof user interaction include levels of confirmation interactions.

In Method 11, which is a variant of Method 6, the analyte is glucose andwherein the initial and new modes of user interaction include differentlevels of control in an artificial pancreas system.

In Method 12, which is a variant of Method 6, the analyte is glucose andwherein the initial and new modes of user interaction are datatransmission modes selected from the group consisting of: on-demand datatransmission and device-initiated data transmission.

In Method 13, which is a variant of Method 6, the initial and new modesof user interaction are selected from the group consisting of: pusheddata or pulled data.

In Method 14, which is a variant of Method 4, which is a variant of anyone of the above Methods, the determined data includes an analyteconcentration value and/or a time rate of change thereof.

In Method 15, which is a variant of any one of the above Methods, thedetermined data indicative of the usability of the device and thetransition criteria include one or more parameters indicative of theusability of a signal from the sensor.

In Method 16, which is a variant of Method 15, the one or moreparameters related to the usability of the signal corresponds to one ormore parameters selected from the group consisting of accuracy,reliability, stability, confidence, and/or glycemic urgency index.

In Method 17, which is a variant of Method 15 or 16, the one or moreparameters related to the usability of the signal correspond to a levelof noise or to one or more faults detected in the signal, and whereinthe transition criteria is a threshold level of noise or a predeterminedtype or level of fault.

In Method 18, which is a variant of Method 17, the level of noise or theone or more faults detected in the signal are determined based on along-term trend of the signal, a short-term trend of the signal, or on ahistory of a user's previous sensor session.

In Method 19, which is a variant of Method 15 or 16, the one or moreparameters related to the usability of the signal correspond to one ormore of the group consisting of: signal value, a range of signal values,or a time rate of change thereof; analyte concentration value or rangeof values; calibration data; a measured error at calibration; data fromself-diagnostics or calibration diagnostics; metadata about sensoridentity; environmental data corresponding to a sensor; historicalpattern data; external data; data about frequency of calibration;biological data about sensor placement; a time duration since sensorimplantation; an impedance associated with the signal; a received userresponse to a prompt displayed on a user interface; a decision supportmode; a data transmission mode; data about a selected use of themonitoring device; data about clinical or user goals; or combinations ofthe above.

In Method 20, which is a variant of Method 19, the environmental datacorresponds to altitude or temperature data about a sensor environment.

In Method 21, which is a variant of Method 19, the calibration data isselected from the group consisting of: calibration values, confidence incalibration values, uncertainty in calibration values, range ofcalibration values, rate of change of calibration values, currentcalibration values compared to historical calibration values, stabilityin calibration values, whether calibration values match expected orpredicted values, confidence in a user's ability to accurately entercalibration values from a meter, whether entered calibration datacorresponds to a default or pre-entered value, or combinations of theabove.

In Method 22, which is a variant of Method 19, the historical patterndata includes data about rebound variability.

In Method 23, which is a variant of Method 19, the external data is froman activity monitor, a sleep monitor, a medicament pump, GPS device, aredundant analyte sensor, or a smart pen, or combinations of the above.

In Method 24, which is a variant of Method 19, the biological data aboutsensor placement corresponds to data about: tissue type, wound response,diffusion distance, or combinations of the above.

In Method 25, which is a variant of Method 24, the diffusion distance isproportional to one or more selected from the group consisting of:impedance, thickness of membrane over electrode array, oxygen depletionrate, or diffusion of specific species between electrodes, orcombinations of the above.

In Method 26, which is a variant of Method 19, the decision support modeis selected from different levels of control of an artificial pancreassystem.

In Method 27, which is a variant of Method 19, the data about a selecteduse of the monitoring device includes data about uses selected from thegroup consisting of: weight loss monitoring, monitoring exercise oractivity impact on glucose, post-meal glucose summary, food selection,effect of the monitored analyte on illness or menstrual cycle orpregnancy, user preference or convenience, or combinations of the above.

In Method 28, which is a variant of Method 19, the data about clinicalor user goals includes: data about user knowledge of device, desiredaccuracy of device, desired convenience of device, use of device forhypoglycemic avoidance, use of device for nighttime control, use ofdevice for postprandial control, qualitative or quantitative desiredduration of sensor session, or combinations of the above.

In Method 29, which is a variant of Method 28, the desired convenienceof the device corresponds to a number of required external metercalibration values.

In Method 30, which is a variant of Method 4, which is a variant of anyone of the above Methods, the initial mode is user-dependentcalibration, and before the causing step, causing the device toperiodically and temporarily enter a self-calibration mode, tointerrogate the sensor and to examine a transient response, followed bya re-entering of the user-dependent calibration initial mode.

The method of any one of the above claims, the Method further comprisesdisplaying output data based on the new mode.

In Method 32, which is a variant of Method 31, the Method furthercomprises displaying an indication of an expected duration of the newmode.

In Method 33, which is a variant of Method 31, the Method furthercomprises displaying an indication of sensor performance.

In Method 34, which is a variant of Method 1-9, the initial mode isuser-dependent calibration and the new mode is device self-calibration;or wherein the initial mode is device self-calibration and the new modeis user-dependent calibration.

In Method 35, which is a variant of Method 34, the determined data issensor signal or data usability and the transition criteria is athreshold level of sensor signal or data usability.

In Method 36, which is a variant of Method 35, the transition criteriais further dependent on a decision support mode, wherein the decisionsupport mode is selected from the group consisting of adjunctive,therapeutic, or a phase or mode of control in an artificial pancreassystem.

In Method 37, which is a variant of Method 35, the transition criteriais further dependent on data entered or received about a user orclinician use of information displayed by the monitoring device.

In Method 38, which is a variant of Method 36, a decision support modeassociated with the initial mode is therapeutic and a decision supportmode associated with the new mode is adjunctive, and wherein thedetermined data is such that the sensor signal usability decreased belowthe threshold level of sensor signal usability associated with thetransition criterion.

In Method 39, which is a variant of Method 38, comprising: prompting auser on a periodic basis to enter a calibration value from an externalmeter for blood glucose; and receiving the calibration value for bloodglucose.

In Method 40, which is a variant of Method 39, the periodicity is lessin the new mode than in the initial mode.

In Method 41, which is a variant of Method 36, a decision support modeassociated with the initial mode is adjunctive and a decision supportmode associated with the new mode is therapeutic, and wherein thedetermined data is such that the sensor signal usability increased abovethe threshold level of sensor signal usability associated with thetransition criterion.

In Method 42, which is a variant of Method 41, the Method furthercomprises prompting a user on a periodic basis to enter a calibrationvalue for blood glucose; and receiving the calibration value for bloodglucose.

In Method 43, which is a variant of Method 42, the periodicity isgreater in the new mode than in the initial mode.

In Method 44, which is a variant of Method 1-9, the Method furthercomprises determining an intended mode of the monitoring device.

In Method 45, which is a variant of Method 44, the determining includesdetecting whether a medicament delivery device is coupled to themonitoring device, and if so, configuring the monitoring device to amode that is therapeutic.

In Method 46, which is a variant of Method 44, the determining includes:prompting a user to indicate an intended use of the monitoring device;receiving the indication; and configuring the monitoring device to amode associated with the received indication.

In Method 47, which is a variant of Method 46, a number of blood glucosecalibration readings required of the user is based on the configuredmode.

In Method 48, which is a variant of Method 46, the intended use istherapeutic, and configuring the monitoring device to a user-dependentcalibration mode.

In Method 49, which is a variant of Method 46, the intended use isadjunctive, and configuring the monitoring device to a deviceself-calibration mode.

In Method 50, which is a variant of Method 1-9, the initial mode isdevice self-calibration and the new mode is user-dependent calibration,and further comprising: prompting a user to enter a calibration valuefor blood glucose; receiving the calibration value for blood glucose;and using the received calibration value to inform the deviceself-calibration.

In Method 51, which is a variant of Method 50, the received calibrationvalue informs the device self-calibration by modifying the deviceself-calibration.

In Method 52, which is a variant of Method 1-9, the initial mode isdevice self-calibration and the new mode is user-dependent calibration,and wherein the determined data and the transition criteria include oneor more parameters related to the usability of a signal from the sensor,and wherein the one or more parameters are selected from the groupconsisting of: data from diagnostic routines indicating a shift insensitivity; data entered by a user about a perceived error; data from aconnected device; data from historic analyte values; time of day; day ofweek; whether a glucose value is high or low as compared to respectivethresholds; glucose urgency index; data about glucose concentrationvalue variability; data about a level of user responsiveness; sensorsignal value trajectory pre-and post-insertion of a new sensor;redundant or overlapping sensor data; user feedback on alerts andalarms; meal or exercise data as compared to predicted signal responsesto meal or exercise data; data about a decision support mode configuredfor the monitoring device; or combinations of the above.

In Method 53, which is a variant of Method 52, the data from diagnosticroutines includes impedance data detecting shifts in sensitivity.

In Method 54, which is a variant of Method 52, the diagnostic routinesare performed on a periodic basis or upon detection of an error.

In Method 55, which is a variant of Method 52, the data entered by auser about a perceived error includes a blood glucose calibration valueentered by a user in the absence of a prompt from the monitoring device,or a detection of a greater than average number of blood glucosecalibration values entered by a user.

In Method 56, which is a variant of Method 52, the data from a connecteddevice includes data from an external blood glucose meter.

In Method 57, which is a variant of Method 1-9, the initial mode isdevice self-calibration and the new mode is user-dependent calibration,and further comprising: if the comparing indicates the determined datahas met or will meet the transition criteria, then before the causingstep, prompting a user to enter a reason for the determined data;receiving the reason for the determined data; and based on the receivedreason, causing the monitoring device to maintain the initial mode ofoperation.

In Method 58, which is a variant of Method 57, the reason is auser-perceived outlier, a user-perceived false alarm, or meal orexercise data.

In Method 59, which is a variant of Method 58, the Method furthercomprises comparing the entered meal or exercise data to prioruser-entered meal or exercise data, and comparing a current signal to asignal associated with the prior user-entered meal or exercise data, anddetermining if the current signal and entered meal or exercise data areconsistent with the prior signal and prior meal or exercise data.

In Method 60, which is a variant of Method 1-9, the initial mode isdevice self-calibration and the new mode is user-dependent calibration,and further comprising: determining if a number of blood glucosemeasurements taken and entered into the monitoring device as calibrationvalues exceed a predetermined threshold over a predetermined period oftime; and if so, causing the monitoring device to transition to auser-dependent calibration mode.

In Method 61, which is a variant of Method 1-9, the initial mode isuser-dependent and the new mode is device self-calibration, and whereinthe transition criteria corresponds to a level of confidence in thedevice self-calibration, and further comprising: prompting a user toenter a calibration value for blood glucose, and using the entered valueas the determined data; and if the comparing indicates the determineddata meets the transition criteria, then performing the causing step.

In Method 62, which is a variant of Method 1-9, the initial mode isuser-dependent calibration and the new mode is device self-calibration,and wherein the determined data and the transition criteria correspondto usability of entered blood glucose data.

In Method 63, which is a variant of Method 62, the usability of enteredblood glucose data corresponds to an accuracy, reliability, stability,or confidence in the blood glucose data.

In Method 64, which is a variant of Method 63, the Method furthercomprises confirming that entered blood glucose data is within aparticular confidence interval or stability criterion, and if it is not,then performing the causing step.

In Method 65, which is a variant of Method 63, the Method furthercomprises confirming that entered blood glucose data is within anexpected range based on an a priori or internal calibration, and if itis not, then performing the causing step.

In Method 66, which is a variant of Method 62, the transition criteriais based at least in part on a decision support mode in which the deviceis configured.

In Method 67, which is a variant of Method 62, the determined data andthe transition criteria indicate that the device continues to requireexternal reference data for calibration, and further comprisingmaintaining the initial mode.

In Method 68, which is a variant of Method 62, the determined data andthe transition criteria indicate that the device no longer requiresexternal reference data for calibration, and further comprisingperforming the causing step.

In Method 69, which is a variant of Method 62, the Method furthercomprises a package of sensors manufactured from the same lot, andwherein the sensor is a first of a plurality of sensors in the pack, andwherein the determined data and the transition criteria indicate thatthe device no longer requires external reference data for calibration,and further comprising: performing the causing step of causing themonitoring device to transition to a new mode of operation; and forsubsequent sensors in the pack, initializing the device in deviceself-calibration mode, using one or more calibration settings associatedwith the first sensor.

In Method 70, which is a variant of Method 1-9, the Method furthercomprises initializing the monitoring device in two modessimultaneously, a first mode being user-dependent calibration and asecond mode being device self-calibration; receiving and comparing twoglucose concentration values, one glucose concentration value from thefirst mode and another glucose concentration value from the second mode;determining and displaying a glucose concentration value based on thetwo glucose concentration values; determining a level of confidence inthe glucose concentration value from the second mode, using at least thetwo glucose concentration values; and once the determined level ofconfidence in the glucose concentration value from the second modereaches a predetermined threshold, then only displaying the glucoseconcentration value from the second mode.

In Method 71, which is a variant of Method 70, the determining a levelof confidence in the glucose concentration value from the second modeincludes comparing at least the glucose concentration value from thesecond mode to a calibration value from an external meter.

In Method 72, which is a variant of Method 70, the Method furthercomprises detecting a fault, and upon detection of the fault, displayingthe glucose concentration value according to the first mode.

In Method 73, which is a variant of Method 70, the comparing includescomparing results of diagnostic tests or internal calibrationinformation.

In Method 74, which is a variant of Method 73, the internal calibrationinformation is based on an impedance measurement.

In Method 75, which is a variant of Method 70, the predeterminedthreshold is based at least in part on a decision support mode in whichthe device is configured.

In Method 76, which is a variant of Method 70, the comparing includescomparing slope and baseline information for the two modes.

In Method 77, which is a variant of Method 76, the comparing furthercomprises: comparing errors in slope and baseline data for each of thetwo modes; and once the error in the slope or baseline for the secondmode is equivalent to that in the first mode, then only displaying theglucose concentration value from the second mode.

In Method 78, which is a variant of Method 70, the comparing furthercomprises determining slope and baseline information for each of the twomodes with respective slope and baseline information for each of the twomodes from a prior session.

In Method 79, which is a variant of Method 70, the Method furthercomprises displaying an indication of when a calibration value from anexternal meter is required.

In Method 80, which is a variant of Method 1-7 or 9, the Method furthercomprises initializing the monitoring device in two parallel modes, afirst mode being user-dependent calibration and a second mode beingdevice self-calibration; receiving and comparing two glucoseconcentration values, one glucose concentration value from the firstmode and another glucose concentration value from the second mode;providing a weighting of the two glucose concentration values; anddisplaying a glucose concentration value according to the weightedglucose concentration values.

In Method 81, which is a variant of Method 80, the weighting isproportional to the usability of the data determined by each of themodes.

In Method 82, which is a variant of Method 81, once the weighting for agiven mode reaches a predetermined threshold, the glucose concentrationvalue displayed is determined based on only the given mode.

In Method 83, which is a variant of Method 1-7 or 11, the determineddata corresponds to a sensor signal, and wherein the transition criteriacorresponds at least to a usability of the sensor signal.

In Method 84, which is a variant of Method 83, the transition criteriais at least in part based on the initial mode of operation.

In Method 85, which is a variant of Method 1-7 or 11, the initial modeis a therapeutic mode, and the new mode is an adjunctive mode.

In Method 86, which is a variant of Method 85, the displaying in the newmode of operation further comprises, while in the adjunctive mode,displaying data to a user in such a way as to indicate its usabilityadjunctively.

In Method 87, which is a variant of Method 86, the displaying in the newmode of operation further comprises indicating the usability of the databy displaying a zone or range of glycemic data instead of a singlevalue.

In Method 88, which is a variant of Method 86, the displaying in the newmode of operation further comprises requiring the user to clear a promptbefore displaying a subsequent glucose concentration value or range ofglucose concentration values.

In Method 89, which is a variant of Method 86, the usability isindicated by colors and/or flashing numerals and/or a dot size on atrend graph.

In Method 90, which is a variant of Method 86, the displaying in the newmode of operation further comprises restricting displayed data to only arate of change arrow and not a glucose concentration value.

In Method 91, which is a variant of Method 86, the usability isindicated by a displayed change in a prediction horizon.

In Method 92, which is a variant of Method 85, the displaying in the newmode of operation further comprises, while in the therapeutic mode,displaying data to a user in such a way as to indicate its usabilitytherapeutically.

In Method 93, which is a variant of Method 92, the displaying in the newmode of operation further comprises indicating the usability of the databy displaying a determined single value of glucose concentration.

In Method 94, which is a variant of Method 92, the usability isindicated by a displayed change in a prediction horizon.

In Method 95, which is a variant of Method 92, the usability isindicated by colors and/or flashing numerals and/or a dot size on atrend graph.

In Method 96, which is a variant of Method 1-7 or 11, the transitioncriteria is further at least partially based on time of day or day ofweek.

In Method 97, which is a variant of Method 83, the usability of thesensor signal is based on one or more parameters according to one ormore of Methods 16, 17, 19, 21, 28, or 52.

In Method 98, which is a variant of Method 83, the usability of thesensor signal is based on one or more parameters selected from the groupconsisting of: a user response to a query about a perceived accuracy orperceived user glucose range; data about likelihood of a potential faultor failure mode; data about glucose context; a user response to a queryabout a glycemic event; a user response to a query about a potentialfalse alarm; a confirmatory meter reading requested of a user via adisplayed prompt; a calibration mode; a data transmission mode; a userindication of desired monitoring device responsiveness; or combinationsof the above.

In Method 99, which is a variant of Method 83, the Method furthercomprises changing a calibration mode along with the change from theinitial to the new mode of operation.

In Method 100, which is a variant of Method 83, the Method furthercomprises transmitting a signal to a medicament delivery pump.

In Method 101, which is a variant of Method 100, the new mode istherapeutic, and the signal instructs the pump to receive and followsignals from the monitoring device.

In Method 102, which is a variant of Method 100, the new mode isadjunctive, and the signal instructs the pump to disregard receivedsignals from the monitoring device.

In Method 103, which is a variant of Method 100, the new mode istherapeutic, and the signal instructs the pump to receive and followsignals from the monitoring device to control the user glucoseconcentration value to a target value.

In Method 104, which is a variant of Method 100, the new mode istherapeutic, and the signal instructs the pump to receive and followsignals from the monitoring device to control the user glucoseconcentration value to a target range of values.

In Method 105, which is a variant of Method 100, the new mode istherapeutic, and the signal instructs the pump to receive and followsignals from the monitoring device to control the user glucoseconcentration value only when the glucose concentration value is below apredetermined value, above a predetermined value, or within apredetermined range of values.

In Method 106, which is a variant of Method 1-7 or 11, the initial modeis adjunctive and the new mode is therapeutic, and wherein in the newmode the monitoring device is configured to calculate a recommendedinsulin bolus and wherein the displaying on the user interface furthercomprises displaying the calculated recommended insulin bolus without acalibration meter reading, and wherein in the initial mode themonitoring device is configured to not calculate and display arecommended insulin bolus without a calibration meter reading.

In Method 107, which is a variant of Method 1-7 or 11, upon a step ofsensor start up, the initial mode is adjunctive, and wherein thedisplaying indicating the new mode of operation further comprisesdisplaying low-resolution data.

In Method 108, which is a variant of Method 107, the Method furthercomprises determining a level of confidence in the sensor over a periodof time, and once the measured level of confidence has reached apredetermined threshold, the step of displaying further comprisesdisplaying high-resolution data and causing a transition to atherapeutic mode.

In Method 109, which is a variant of Method 108, the determining a levelof confidence includes receiving an external blood glucose meterreading.

In Method 110, which is a variant of Method 109, the external bloodglucose meter reading correlates to what the monitoring device estimatesthe glucose concentration value to be or is used to calibrate themonitoring device.

In Method 111, which is a variant of Method 109, the Method furthercomprises configuring the monitoring device to enter a user-dependentcalibration mode of operation.

In Method 112, which is a variant of Method 107, the Method furthercomprises receiving an external blood glucose meter reading, developinga level of confidence in the sensor over a period of time, and once thelevel of confidence has reached a predetermined threshold, causing themonitoring device to enter a user-dependent calibration mode ofoperation.

In Method 113, which is a variant of Method 1-7 or 11, the monitoringdevice operates in two modes of operation concurrently, one adjunctiveand one therapeutic, and wherein the displaying further comprisesdisplaying an initial splash screen with data displayed in theadjunctive mode of operation.

In Method 114, which is a variant of Method 113, upon receiving aselection from a user interface for data requiring a new mode ofoperation, causing a transition to the new mode of operation, receivingone or more data values required by the new mode of operation, anddisplaying the data using the new mode of operation.

In Method 115, which is a variant of Method 114, the selected dataincludes a hypoglycemic safety alarm, and wherein the new mode ofoperation is user-dependent calibration.

In Method 116, which is a variant of Method 1-7 or 12, the determineddata includes data based on a glucose concentration value, and whereinthe transition criteria is selected from the group consisting of: aglycemic state threshold, a GUI threshold, a glucose threshold, aglucose rate of change threshold, a glucose acceleration threshold, apredicted value of glucose or any of its rates of change, an excursionbeyond a predetermined threshold, an alert criteria, a criteria for aglycemic danger zone, or a combination of the above.

In Method 117, which is a variant of Method 1-7 or 12, the transitioncriteria is selected from the group consisting of: a duration of timesince a user last requested a glucose concentration value, a decisionsupport mode, a user response to a query, a calibration mode of themonitoring device, or a combination of the above.

In Method 118, which is a variant of Method 1-7 or 12, the determiningdata includes transmitting a signal to cause a sensor to send a glucoseconcentration value.

In Method 119, which is a variant of Method 1-7 or 12, the determiningdata includes receiving a signal from a sensor corresponding to aglucose concentration value.

In Method 120, which is a variant of Method 1-7 or 12, the initial modeis on-demand transmission, the new mode is device-initiatedtransmission, the determined data is a glucose concentration value, andthe transition criteria is the glucose concentration value being in adangerous range for a period exceeding a first predetermined duration oftime.

In Method 121, which is a variant of Method 120, the Method furthercomprises displaying an alert to the user on a user interface of themonitoring device until the user performs an action of responding to thealert.

In Method 122, which is a variant of Method 1-7 or 12, the initial modeis device-initiated transmission, the new mode is on-demandtransmission, the determined data is a glucose concentration value, andthe transition criteria is the glucose concentration value being in adangerous range for a period exceeding a second predetermined durationof time.

System 123 is a system for performing any one of Methods 1-122.

Device 124 is substantially as shown and/or described in thespecification and/or drawings.

System 125 is substantially as shown and/or described in thespecification and/or drawings.

Method 126 is substantially as shown and/or described in thespecification and/or drawings.

Electronic Device 127 is provided, for monitoring data associated with aphysiological condition, comprising: a continuous analyte sensor,wherein the continuous analyte sensor is configured to substantiallycontinuously measure the concentration of analyte in the host, and toprovide continuous sensor data indicative of the analyte concentrationin the host; and a processor module configured to perform any one ofMethods 1-122.

In Electronic Device 128, which is a variant of Electronic Device 127,the analyte is glucose.

Electronic Device 129 is provided, for delivering a medicament to ahost, the device comprising: a medicament delivery device configured todeliver medicament to the host, wherein the medicament delivery deviceis operably connected to a continuous analyte sensor, wherein thecontinuous analyte sensor is configured to substantially continuouslymeasure the concentration of analyte in the host, and to providecontinuous sensor data indicative of the analyte concentration in thehost; and a processor module configured to perform any one of Methods1-122.

In Electronic Device 130, which is a variant of Electronic Device 129,the analyte is glucose and the medicament is insulin.

1-20. (canceled)
 21. A method of operating a continuous glucosemonitoring device, the continuous glucose monitoring device coupled to aglucose sensor and operating in an initial transmission mode,comprising: measuring glucose concentration data; transmitting themeasured glucose concentration data to a continuous glucose monitoringdevice using a transmission scheme defined by an initial transmissionmode; determining user data; comparing the determined user data to oneor more transition criteria associated with one or more transmissionmodes; if the comparing indicates the determined user data has met orwill meet the transition criteria, causing the continuous glucosemonitoring device to transition to a new transmission mode; andtransmitting the glucose concentration data using a transmission schemedefined by the new transmission mode, wherein the initial transmissionmode and the new transmission mode are selected from one pair of modes,a group of possible pairs consisting of: on-demand data transmission anddevice-initiated data transmission, or periodic data transmission andnonperiodic data transmission, or scheduled data transmission andunscheduled data transmission, or pushed data transmission and pulleddata transmission; and wherein the new transmission mode is differentfrom the initial transmission mode, whereby the mode of datatransmission can be modified in real time for a user.
 22. The method ofclaim 21, wherein the determined user data is indicative of a userindication of transmission mode or of a user glycemic state.
 23. Themethod of claim 22, wherein the user indication of transmission modeindicates a user desire for additional data, and wherein the determininguser data, and the comparing the determined user data to one or moretransition criteria, indicates that a device swipe has occurred, whereinthe continuous glucose monitoring device exchanges data with the sensorelectronics at least temporarily using an NFC protocol.
 24. The methodof claim 21, further comprising displaying the glucose concentrationdata on a user interface of the continuous glucose monitoring device,and wherein the user interface also displays an indication of the newtransmission mode.
 25. The method of claim 21, wherein the initial andnew transmission modes are configured such that the new transmissionmode requires less user interaction than the initial transmission mode.26. The method of claim 21, wherein the analyte is glucose and whereinthe initial and new transmission modes are selected from the groupconsisting of: on-demand data transmission and device-initiated datatransmission.
 27. The method of claim 21, wherein the analyte is glucoseand wherein the initial and new transmission modes are selected from thegroup consisting of: scheduled data transmission and unscheduled datatransmission.
 28. The method of claim 27, wherein the transmission modeis scheduled data transmission, and wherein the scheduled datatransmission is further selected from the group consisting of periodictransmissions and nonperiodic transmissions.
 29. The method of claim 27,wherein the transmission mode is unscheduled data transmission, andwherein the unscheduled data transmission is further selected from thegroup consisting of: event driven unscheduled transmissions and userdriven unscheduled transmissions.
 30. The method of claim 29, whereinthe transmission mode is a user driven unscheduled transmission, wherebythe user pulls data from the sensor to view their current status. 31.The method of claim 30, wherein the user driven unscheduled transmissionexchanges data using NFC.
 32. The method of claim 21, wherein theinitial and new transmission modes are selected from the groupconsisting of: pushed data or pulled data.
 33. The method of claim 21,wherein the transition criteria is selected from the group consistingof: a duration of time since a user last requested a glucoseconcentration value, a decision support mode switch, a user response toa query, a calibration mode of the monitoring device, or a combinationof the above.
 34. The method of claim 33, wherein the decision supportmode switch is to a non-therapeutic mode, and wherein the newtransmission mode has a mode of user interaction that is on demand orunscheduled.
 35. The method of claim 33, wherein the decision supportmode switch is to a therapeutic mode, and wherein the new transmissionmode has a mode of user interaction that is automatic or scheduled. 36.The method of claim 35, wherein the displayed glucose concentration datahas a higher resolution in the new transmission mode than in the initialmode.
 37. The method of claim 21, wherein the determining data includestransmitting a signal to cause a sensor to send a glucose concentrationvalue.
 38. The method of claim 21, wherein the initial mode is on-demandtransmission, the new mode is device-initiated transmission, thedetermined data is a glucose concentration value, and the transitioncriteria is the glucose concentration value being in a dangerous rangefor a period exceeding a first predetermined duration of time.
 39. Themethod of claim 38, further comprising displaying an alert to the useron a user interface of the monitoring device until the user performs anaction of responding to the alert.
 40. The method of claim 21, whereinthe initial mode is on-demand transmission, the new mode isdevice-initiated transmission, the determined data is a glucoseconcentration rate of change value, and the transition criteria is athreshold level of the glucose concentration rate of change.
 41. Themethod of claim 21, wherein the initial mode is on-demand transmission,the new mode is device-initiated transmission, the determined data is aglucose concentration value, and the transition criteria is theoccurrence of a glucose excursion exceeding a predetermined threshold.42. An electronic device for monitoring data associated with aphysiological condition, comprising: a continuous analyte sensor,wherein the continuous analyte sensor is configured to substantiallycontinuously measure the concentration of analyte in the host, and toprovide continuous sensor data indicative of the analyte concentrationin the host; and a processor module configured to perform the method ofclaim
 21. 43. A system for operating a continuous glucose monitoringdevice, the continuous glucose monitoring device coupled to a glucosesensor and operating in an initial transmission mode, comprising: meansfor measuring glucose concentration data; means for transmitting themeasured glucose concentration data to a continuous glucose monitoringdevice using a transmission scheme defined by a transmission mode; meansfor determining user data; means for comparing the determined user datato one or more transition criteria associated with one or moretransmission modes; and means for, if the comparing indicates thedetermined user data has met or will meet the transition criteria,causing the continuous glucose monitoring device to transition from aninitial transmission mode to a new transmission mode; wherein theinitial transmission mode and the new transmission mode are selectedfrom one pair of modes, a group of possible pairs consisting of:on-demand data transmission and device-initiated data transmission, orperiodic data transmission and nonperiodic data transmission, orscheduled data transmission and unscheduled data transmission, or pusheddata transmission and pulled data transmission, and wherein the newtransmission mode is different from the initial transmission mode,whereby the mode of data transmission can be modified in real time for auser.
 44. The system of claim 43, wherein the means for determining userdata is the same as the means for measuring a glucose concentrationdata.